import pandas as pd
import requests
import numpy as np
import warnings
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import explained_variance_score,max_error,mean_absolute_error,mean_squared_error,r2_score
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.neural_network import MLPRegressor
warnings.filterwarnings('ignore')
pd.options.display.float_format = "{:.2f}".format
import plotly.graph_objects as go
from pyecharts import options as opts
from pyecharts.globals import ThemeType
#resp = requests.get('https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_8_Direct_investment_related_Indicators/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson')
resp = requests.get('https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Indicator_7_1_Trade_related_Indicators_CO2_emissions_embodied_in_trade/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson')
txt = resp.json()
Basic_df = pd.DataFrame(txt['features'])
basic_list = Basic_df['properties'].values.tolist()
Actual_df = pd.DataFrame(basic_list)
Actual_df.head(5)
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Argentina | AR | ARG | C02 Emissions Embodied in Final Demand, balance | ECBPDTCEFDB | Millions of metric tons | 11.10 | 8.05 | 1.59 | ... | -5.80 | -13.37 | -13.55 | -20.51 | -14.60 | -21.00 | NaN | NaN | NaN | NaN |
| 1 | 2 | Argentina | AR | ARG | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 141.18 | 153.44 | 166.89 | ... | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 | NaN | NaN | NaN | NaN |
| 2 | 3 | Argentina | AR | ARG | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 39.18 | 40.23 | 39.51 | ... | 34.34 | 34.41 | 30.52 | 26.57 | 27.17 | 21.29 | NaN | NaN | NaN | NaN |
| 3 | 4 | Argentina | AR | ARG | C02 Emissions Embodied in Gross Exports, balance | ECBPDTCETB | Millions of metric tons | 11.04 | 7.99 | 1.53 | ... | -5.80 | -13.37 | -13.54 | -20.49 | -14.59 | -20.95 | NaN | NaN | NaN | NaN |
| 4 | 5 | Argentina | AR | ARG | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 28.14 | 32.24 | 37.97 | ... | 40.14 | 47.78 | 44.06 | 47.05 | 41.76 | 42.24 | NaN | NaN | NaN | NaN |
5 rows × 22 columns
Actual_df.to_csv("working on.csv")
Actual_df.shape
(396, 22)
Actual_df.isnull().sum()
ObjectId 0 Country 0 ISO2 12 ISO3 0 Indicator 0 Code 0 Unit 0 F2005 0 F2006 0 F2007 0 F2008 0 F2009 0 F2010 0 F2011 0 F2012 0 F2013 0 F2014 0 F2015 0 F2016 225 F2017 225 F2018 225 F2019 225 dtype: int64
Actual_df['Indicator'].unique()
array(['C02 Emissions Embodied in Final Demand, balance',
'C02 Emissions Embodied in Final Domestic Demand',
'C02 Emissions Embodied in Gross Exports',
'C02 Emissions Embodied in Gross Exports, balance',
'C02 Emissions Embodied in Gross Imports',
'C02 Emissions Embodied in Production'], dtype=object)
CO2_FINAL_DF=Actual_df[Actual_df['Indicator']=='C02 Emissions Embodied in Final Domestic Demand']
CO2_FINAL_DF.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Argentina | AR | ARG | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 141.18 | 153.44 | 166.89 | ... | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 | NaN | NaN | NaN | NaN |
| 7 | 8 | Australia | AU | AUS | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 418.42 | 420.98 | 444.59 | ... | 446.21 | 450.35 | 469.37 | 448.02 | 423.10 | 426.40 | NaN | NaN | NaN | NaN |
| 13 | 14 | Austria | AT | AUT | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 95.26 | 95.39 | 94.53 | ... | 89.74 | 90.34 | 86.19 | 86.53 | 82.95 | 83.44 | NaN | NaN | NaN | NaN |
| 19 | 20 | Belgium | BE | BEL | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 131.71 | 129.89 | 129.69 | ... | 127.70 | 122.48 | 117.93 | 117.66 | 112.33 | 117.83 | NaN | NaN | NaN | NaN |
| 25 | 26 | Brazil | BR | BRA | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 312.82 | 330.87 | 363.68 | ... | 458.99 | 486.29 | 510.59 | 542.57 | 555.96 | 475.38 | NaN | NaN | NaN | NaN |
5 rows × 22 columns
CO2_FINAL_DF.shape
(66, 22)
CO2_FINAL_DF.columns
Index(['ObjectId', 'Country', 'ISO2', 'ISO3', 'Indicator', 'Code', 'Unit',
'F2005', 'F2006', 'F2007', 'F2008', 'F2009', 'F2010', 'F2011', 'F2012',
'F2013', 'F2014', 'F2015', 'F2016', 'F2017', 'F2018', 'F2019'],
dtype='object')
CO2_FINAL_DF.isnull().sum()
ObjectId 0 Country 0 ISO2 2 ISO3 0 Indicator 0 Code 0 Unit 0 F2005 0 F2006 0 F2007 0 F2008 0 F2009 0 F2010 0 F2011 0 F2012 0 F2013 0 F2014 0 F2015 0 F2016 66 F2017 66 F2018 66 F2019 66 dtype: int64
CO2_FINAL_DF_FINAL=CO2_FINAL_DF[['Country','F2005', 'F2006', 'F2007', 'F2008', 'F2009', 'F2010', 'F2011', 'F2012',
'F2013', 'F2014', 'F2015']]
CO2_FINAL_DF_FINAL.head()
| Country | F2005 | F2006 | F2007 | F2008 | F2009 | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Argentina | 141.18 | 153.44 | 166.89 | 179.93 | 167.85 | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 |
| 7 | Australia | 418.42 | 420.98 | 444.59 | 441.38 | 434.03 | 446.21 | 450.35 | 469.37 | 448.02 | 423.10 | 426.40 |
| 13 | Austria | 95.26 | 95.39 | 94.53 | 92.34 | 86.54 | 89.74 | 90.34 | 86.19 | 86.53 | 82.95 | 83.44 |
| 19 | Belgium | 131.71 | 129.89 | 129.69 | 137.20 | 128.67 | 127.70 | 122.48 | 117.93 | 117.66 | 112.33 | 117.83 |
| 25 | Brazil | 312.82 | 330.87 | 363.68 | 410.89 | 385.40 | 458.99 | 486.29 | 510.59 | 542.57 | 555.96 | 475.38 |
CO2_FINAL_DF_FINAL=CO2_FINAL_DF_FINAL.T
CO2_FINAL_DF_FINAL.columns=CO2_FINAL_DF_FINAL.iloc[0]
CO2_FINAL_DF_FINAL=CO2_FINAL_DF_FINAL.iloc[1:]
CO2_FINAL_DF_FINAL.head()
| Country | Argentina | Australia | Austria | Belgium | Brazil | Brunei Darussalam | Bulgaria | Cambodia | Canada | Chile | ... | Sweden | Switzerland | Taiwan Province of China | Thailand | Tunisia | Turkey | United Kingdom | United States | Vietnam | World |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F2005 | 141.18 | 418.42 | 95.26 | 131.71 | 312.82 | 4.08 | 39.27 | 6.16 | 539.81 | 61.15 | ... | 82.66 | 92.42 | 235.15 | 196.76 | 21.6 | 281.6 | 737.36 | 6798.77 | 78.15 | 27069.6 |
| F2006 | 153.44 | 420.98 | 95.39 | 129.89 | 330.87 | 5.74 | 42.33 | 6.64 | 553.83 | 65.12 | ... | 86.17 | 94.04 | 242.15 | 192.62 | 22.32 | 309.11 | 751.51 | 6741.17 | 81.58 | 27924.3 |
| F2007 | 166.89 | 444.59 | 94.53 | 129.69 | 363.68 | 6.36 | 46.07 | 7.23 | 579.08 | 71.81 | ... | 86.24 | 92.53 | 230.72 | 195.25 | 23.02 | 339.3 | 749.3 | 6714.57 | 96.4 | 28979.7 |
| F2008 | 179.93 | 441.38 | 92.34 | 137.2 | 410.89 | 5.54 | 45.71 | 7.04 | 570.42 | 79.94 | ... | 82.49 | 95.08 | 223.91 | 209.03 | 24.18 | 334.4 | 697.83 | 6393.67 | 106.85 | 29202.9 |
| F2009 | 167.85 | 434.03 | 86.54 | 128.67 | 385.4 | 6.08 | 39.56 | 7.69 | 530.4 | 70.01 | ... | 71.74 | 97.2 | 207.12 | 196.47 | 24.01 | 299.31 | 616.99 | 5873.2 | 112.55 | 28815.2 |
5 rows × 66 columns
i=0
VVIP1=list()
while i<len(CO2_FINAL_DF_FINAL.T):
series=CO2_FINAL_DF_FINAL[CO2_FINAL_DF_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = LinearRegression()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP1.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([205.328], dtype=object), array([203.334], dtype=object), array([216.033], dtype=object)]
Predicted Values: [[205.40652461]
[207.11672765]
[205.52320201]]
Explained Variance Score: -0.17964816561064967
Maximam Error : 10.50979798706851
Mean Absolute Error : 4.79035008201032
Mean Squared Error : 41.59034943842105
r2 score : -0.33760817707871804
Origional Values: [array([448.017], dtype=object), array([423.099], dtype=object), array([426.401], dtype=object)]
Predicted Values: [[469.58687023]
[452.8302721 ]
[433.27698409]]
Explained Variance Score: 0.2676633239331133
Maximam Error : 29.731272102026367
Mean Absolute Error : 19.39237547464802
Mean Squared Error : 465.49566663328915
r2 score : -2.8118535149075923
Origional Values: [array([86.533], dtype=object), array([82.95], dtype=object), array([83.435], dtype=object)]
Predicted Values: [[86.80958233]
[87.03928856]
[84.63274638]]
Explained Variance Score: -0.047443147503038796
Maximam Error : 4.0892885644208405
Mean Absolute Error : 1.8545390920710219
Mean Squared Error : 6.077791712694474
r2 score : -1.4128095728706214
Origional Values: [array([117.656], dtype=object), array([112.33], dtype=object), array([117.828], dtype=object)]
Predicted Values: [[118.41666769]
[118.19695553]
[113.98764998]]
Explained Variance Score: -1.4132718495978955
Maximam Error : 5.866955532529985
Mean Absolute Error : 3.4893244151556786
Mean Squared Error : 16.582690286951067
r2 score : -1.545792829052675
Origional Values: [array([542.574], dtype=object), array([555.957], dtype=object), array([475.385], dtype=object)]
Predicted Values: [[530.40701967]
[560.10468644]
[572.5302437 ]]
Explained Variance Score: -0.8652920324972373
Maximam Error : 97.14524369674609
Mean Absolute Error : 37.81997015483429
Mean Squared Error : 3200.8123619899943
r2 score : -1.5754585256655398
Origional Values: [array([6.635], dtype=object), array([5.438], dtype=object), array([6.39], dtype=object)]
Predicted Values: [[5.75405002]
[5.71399662]
[5.80584321]]
Explained Variance Score: 0.0969775221468141
Maximam Error : 0.8809499798157736
Mean Absolute Error : 0.5803677994299488
Mean Squared Error : 0.39782872136462144
r2 score : -0.49239372319624497
Origional Values: [array([32.037], dtype=object), array([33.911], dtype=object), array([34.763], dtype=object)]
Predicted Values: [[37.55389468]
[35.06812267]
[36.22403749]]
Explained Variance Score: -2.0465671809372195
Maximam Error : 5.516894678062442
Mean Absolute Error : 2.7116849455158154
Mean Squared Error : 11.303230102479596
r2 score : -7.717998655765086
Origional Values: [array([9.679], dtype=object), array([10.972], dtype=object), array([12.595], dtype=object)]
Predicted Values: [[ 9.48245083]
[10.0343093 ]
[11.29056456]]
Explained Variance Score: 0.8507924578832489
Maximam Error : 1.3044354407228607
Mean Absolute Error : 0.8128917705120262
Mean Squared Error : 0.8731490839028321
r2 score : 0.38650004714442276
Origional Values: [array([561.804], dtype=object), array([563.554], dtype=object), array([547.864], dtype=object)]
Predicted Values: [[559.3996331 ]
[559.17667259]
[559.07781754]]
Explained Variance Score: 0.02509429044260958
Maximam Error : 11.213817538403987
Mean Absolute Error : 5.99850394889783
Mean Squared Error : 50.23055973897269
r2 score : -0.019191981757592735
Origional Values: [array([99.348], dtype=object), array([89.911], dtype=object), array([89.237], dtype=object)]
Predicted Values: [[ 97.54843014]
[100.41933611]
[ 92.47425117]]
Explained Variance Score: -0.1980730116619578
Maximam Error : 10.508336105062313
Mean Absolute Error : 5.181719046487875
Mean Squared Error : 41.38112484466537
r2 score : -0.9423343967744306
Origional Values: [array([107.616], dtype=object), array([112.459], dtype=object), array([104.308], dtype=object)]
Predicted Values: [[101.94026931]
[110.37514545]
[115.02089834]]
Explained Variance Score: -3.4155285179521773
Maximam Error : 10.71289833955663
Mean Absolute Error : 6.157494525629555
Mean Squared Error : 50.44085315466126
r2 score : -3.502025712601432
Origional Values: [array([7782.052], dtype=object), array([7783.586], dtype=object), array([7977.936], dtype=object)]
Predicted Values: [[7850.04154327]
[8267.31290898]
[8268.87434684]]
Explained Variance Score: -2.410752902215053
Maximam Error : 483.72690897886514
Mean Absolute Error : 280.8849330288037
Mean Squared Error : 107753.14070852748
r2 score : -11.73597210103376
Origional Values: [array([99.774], dtype=object), array([103.908], dtype=object), array([97.378], dtype=object)]
Predicted Values: [[ 96.60370542]
[104.31379702]
[108.52096686]]
Explained Variance Score: -4.085324537420509
Maximam Error : 11.142966856390956
Mean Absolute Error : 4.906352818766895
Mean Squared Error : 44.79371643425021
r2 score : -5.157524859121542
Origional Values: [array([12.926], dtype=object), array([13.348], dtype=object), array([13.546], dtype=object)]
Predicted Values: [[11.93275053]
[12.14826048]
[12.32215187]]
Explained Variance Score: 0.8397922062904569
Maximam Error : 1.2238481307520193
Mean Absolute Error : 1.1389457091721737
Mean Squared Error : 1.3079078958840953
r2 score : -18.56357956774421
Origional Values: [array([19.095], dtype=object), array([16.877], dtype=object), array([17.126], dtype=object)]
Predicted Values: [[17.35076803]
[17.4449738 ]
[14.86536395]]
Explained Variance Score: -0.5368317297556462
Maximam Error : 2.260636051772977
Mean Absolute Error : 1.5242806072244501
Mean Squared Error : 2.825138257061019
r2 score : -1.8702697406248872
Origional Values: [array([7.979], dtype=object), array([7.936], dtype=object), array([7.929], dtype=object)]
Predicted Values: [[9.01493671]
[7.84287707]
[7.80146494]]
Explained Variance Score: -596.9141453646688
Maximam Error : 1.0359367105017698
Mean Absolute Error : 0.4188648978228926
Mean Squared Error : 0.36603397943163013
r2 score : -748.0463426295313
Origional Values: [array([95.808], dtype=object), array([93.444], dtype=object), array([91.848], dtype=object)]
Predicted Values: [[108.69415367]
[109.39026315]
[109.92576683]]
Explained Variance Score: -0.7155766907018415
Maximam Error : 18.07776683357281
Mean Absolute Error : 15.636727882739445
Mean Squared Error : 249.0473061346605
r2 score : -93.10909825642523
Origional Values: [array([66.926], dtype=object), array([62.631], dtype=object), array([59.383], dtype=object)]
Predicted Values: [[63.50081541]
[65.64590747]
[61.56386476]]
Explained Variance Score: 0.14314462900479497
Maximam Error : 3.425184588933327
Mean Absolute Error : 2.8736522733949266
Mean Squared Error : 8.525909207927894
r2 score : 0.10664611570695182
Origional Values: [array([15.476], dtype=object), array([14.749], dtype=object), array([13.291], dtype=object)]
Predicted Values: [[14.85728442]
[15.10576271]
[14.97561594]]
Explained Variance Score: -0.07963502215669793
Maximam Error : 1.68461594283281
Mean Absolute Error : 0.8866980788431166
Mean Squared Error : 1.1160064923590205
r2 score : -0.35209451349935206
Origional Values: [array([58.446], dtype=object), array([55.964], dtype=object), array([52.286], dtype=object)]
Predicted Values: [[61.53283467]
[61.64400015]
[60.30461799]]
Explained Variance Score: 0.36640913979113177
Maximam Error : 8.018617986067952
Mean Absolute Error : 5.595150935896605
Mean Squared Error : 35.36306146987139
r2 score : -4.522256271528982
Origional Values: [array([475.829], dtype=object), array([448.16], dtype=object), array([445.014], dtype=object)]
Predicted Values: [[467.2462545 ]
[468.14962528]
[441.12760763]]
Explained Variance Score: 0.18349468796633173
Maximam Error : 19.989625283544285
Mean Absolute Error : 10.819587719573576
Mean Squared Error : 162.78422834299548
r2 score : 0.15070826478349542
Origional Values: [array([26760.591], dtype=object), array([26772.893], dtype=object), array([26687.974], dtype=object)]
Predicted Values: [[26510.50346801]
[26829.09465804]
[26839.20188268]]
Explained Variance Score: -19.88486841679963
Maximam Error : 250.0875319948791
Mean Absolute Error : 152.50569090747013
Mean Squared Error : 29524.090842353082
r2 score : -20.028880159430624
Origional Values: [array([898.539], dtype=object), array([864.752], dtype=object), array([853.444], dtype=object)]
Predicted Values: [[918.38915785]
[918.06407854]
[918.65617851]]
Explained Variance Score: -0.004854441246785246
Maximam Error : 65.2121785143288
Mean Absolute Error : 46.124804966686305
Mean Squared Error : 2496.2782371038065
r2 score : -5.801866059899281
Origional Values: [array([78.272], dtype=object), array([78.306], dtype=object), array([72.95], dtype=object)]
Predicted Values: [[80.90384929]
[68.84041137]
[68.87816861]]
Explained Variance Score: -2.865529970613938
Maximam Error : 9.465588625258434
Mean Absolute Error : 5.389756432229234
Mean Squared Error : 37.70126984004797
r2 score : -4.951623378389537
Origional Values: [array([46.072], dtype=object), array([46.181], dtype=object), array([48.26], dtype=object)]
Predicted Values: [[45.00908893]
[43.05774279]
[43.16777731]]
Explained Variance Score: -1.670316752995229
Maximam Error : 5.092222692903874
Mean Absolute Error : 3.0927969916844993
Mean Squared Error : 12.271749173070058
r2 score : -11.108332427882837
Origional Values: [array([2.547], dtype=object), array([2.716], dtype=object), array([2.881], dtype=object)]
Predicted Values: [[2.49259094]
[2.46695421]
[2.60671572]]
Explained Variance Score: 0.48091006291283844
Maximam Error : 0.2742842828744907
Mean Absolute Error : 0.1925797129288466
Mean Squared Error : 0.04673867341531692
r2 score : -1.5137028405173427
Origional Values: [array([1696.941], dtype=object), array([1858.186], dtype=object), array([1918.813], dtype=object)]
Predicted Values: [[1844.05459448]
[1805.88446312]
[1970.13796133]]
Explained Variance Score: 0.24362212060746513
Maximam Error : 147.113594480988
Mean Absolute Error : 83.58003089746983
Mean Squared Error : 9004.037365600214
r2 score : -0.027040480568370162
Origional Values: [array([475.289], dtype=object), array([496.938], dtype=object), array([484.588], dtype=object)]
Predicted Values: [[503.68854064]
[509.03185888]
[532.68295202]]
Explained Variance Score: -1.7553162460194316
Maximam Error : 48.09495202014324
Mean Absolute Error : 29.529450510915183
Mean Squared Error : 1088.639913566763
r2 score : -12.845035503554957
Origional Values: [array([47.516], dtype=object), array([48.607], dtype=object), array([46.672], dtype=object)]
Predicted Values: [[46.44131261]
[45.36618918]
[46.46138961]]
Explained Variance Score: -1.5892054995278895
Maximam Error : 3.240810817766011
Mean Absolute Error : 1.5087028685427721
Mean Squared Error : 3.9007214968119777
r2 score : -5.217013592961821
Origional Values: [array([90.278], dtype=object), array([89.165], dtype=object), array([88.27], dtype=object)]
Predicted Values: [[100.77582907]
[ 92.38072697]
[ 91.29487588]]
Explained Variance Score: -16.936925508925682
Maximam Error : 10.497829070925746
Mean Absolute Error : 5.579477304595419
Mean Squared Error : 43.23172973102598
r2 score : -63.08014936766146
Origional Values: [array([436.164], dtype=object), array([418.783], dtype=object), array([423.012], dtype=object)]
Predicted Values: [[453.08232126]
[425.95496885]
[409.56919952]]
Explained Variance Score: -1.9247030134538132
Maximam Error : 16.9183212596605
Mean Absolute Error : 12.511030194754653
Mean Squared Error : 172.79187203015167
r2 score : -2.154679751758699
Origional Values: [array([1497.445], dtype=object), array([1448.543], dtype=object), array([1361.019], dtype=object)]
Predicted Values: [[1483.74325614]
[1467.37606372]
[1443.42739541]]
Explained Variance Score: 0.49980670657018234
Maximam Error : 82.40839540930642
Mean Absolute Error : 38.31440099663473
Mean Squared Error : 2444.521902621587
r2 score : 0.23246000327142857
Origional Values: [array([181.262], dtype=object), array([163.473], dtype=object), array([180.186], dtype=object)]
Predicted Values: [[167.19836087]
[181.85315413]
[167.07038593]]
Explained Variance Score: -2.426665782206629
Maximam Error : 18.380154132145606
Mean Absolute Error : 15.186469111512062
Mean Squared Error : 235.87844799777758
r2 score : -2.556369529824089
Origional Values: [array([582.966], dtype=object), array([576.605], dtype=object), array([584.818], dtype=object)]
Predicted Values: [[576.34744124]
[576.22005817]
[575.46065448]]
Explained Variance Score: -0.1393621985092497
Maximam Error : 9.357345517228055
Mean Absolute Error : 5.453615369512704
Mean Squared Error : 43.83780512904712
r2 score : -2.543384448256002
Origional Values: [array([9.864], dtype=object), array([9.377], dtype=object), array([9.229], dtype=object)]
Predicted Values: [[11.02926731]
[10.95529758]
[10.77150547]]
Explained Variance Score: 0.5256157873651193
Maximam Error : 1.5782975837535531
Mean Absolute Error : 1.4286901189701482
Mean Squared Error : 2.076064757738454
r2 score : -27.211745799694416
Origional Values: [array([14.109], dtype=object), array([13.952], dtype=object), array([14.205], dtype=object)]
Predicted Values: [[15.57633502]
[15.4209471 ]
[15.3625837 ]]
Explained Variance Score: -0.9708569259907125
Maximam Error : 1.4689471028688317
Mean Absolute Error : 1.3646219407393143
Mean Squared Error : 1.883625891233179
r2 score : -172.2087481976684
Origional Values: [array([10.014], dtype=object), array([9.232], dtype=object), array([9.147], dtype=object)]
Predicted Values: [[10.47499441]
[10.3458495 ]
[10.10131848]]
Explained Variance Score: 0.4928347225340687
Maximam Error : 1.1138495048146133
Mean Absolute Error : 0.8430541305050264
Mean Squared Error : 0.7879667745749034
r2 score : -4.17476965114375
Origional Values: [array([201.847], dtype=object), array([210.804], dtype=object), array([209.462], dtype=object)]
Predicted Values: [[187.63979925]
[208.64792305]
[217.40285558]]
Explained Variance Score: -4.268744726711009
Maximam Error : 14.207200750210063
Mean Absolute Error : 8.101377760256847
Mean Squared Error : 89.8501361078378
r2 score : -4.775377314941789
Origional Values: [array([2.979], dtype=object), array([2.846], dtype=object), array([2.597], dtype=object)]
Predicted Values: [[3.08815242]
[3.05882352]
[3.00749793]]
Explained Variance Score: 0.3766699471475775
Maximam Error : 0.410497933138108
Mean Absolute Error : 0.24415795811711508
Mean Squared Error : 0.07523888479461298
r2 score : -2.0013650001840153
Origional Values: [array([492.039], dtype=object), array([486.441], dtype=object), array([485.486], dtype=object)]
Predicted Values: [[482.08553003]
[481.19452954]
[480.05861045]]
Explained Variance Score: 0.4324622432876565
Maximam Error : 9.953469969511048
Mean Absolute Error : 6.875776660527113
Mean Squared Error : 52.01785802394886
r2 score : -5.22625041717568
Origional Values: [array([61.914], dtype=object), array([63.151], dtype=object), array([66.593], dtype=object)]
Predicted Values: [[63.52071316]
[63.69962678]
[64.8831356 ]]
Explained Variance Score: 0.5117738665918252
Maximam Error : 1.709864400645941
Mean Absolute Error : 1.2884014499738707
Mean Squared Error : 1.935384936837097
r2 score : 0.5061474068572329
Origional Values: [array([178.633], dtype=object), array([168.994], dtype=object), array([179.238], dtype=object)]
Predicted Values: [[175.76709313]
[176.61549126]
[167.5593344 ]]
Explained Variance Score: -1.825940512410439
Maximam Error : 11.678665601678262
Mean Absolute Error : 7.388687913146508
Mean Squared Error : 67.56392717798256
r2 score : -2.0677427987422448
Origional Values: [array([43.508], dtype=object), array([43.891], dtype=object), array([42.83], dtype=object)]
Predicted Values: [[41.48378473]
[42.25494103]
[42.40269133]]
Explained Variance Score: -1.4027872141531588
Maximam Error : 2.0242152693764766
Mean Absolute Error : 1.3625276353255014
Mean Squared Error : 2.318909703349192
r2 score : -11.049107802545763
Origional Values: [array([63.328], dtype=object), array([63.259], dtype=object), array([59.565], dtype=object)]
Predicted Values: [[63.12608909]
[63.208187 ]
[63.20510833]]
Explained Variance Score: -0.021440650307775533
Maximam Error : 3.640108327632113
Mean Absolute Error : 1.2976107447840235
Mean Squared Error : 4.431246203945084
r2 score : -0.43403107421494624
Origional Values: [array([59.951], dtype=object), array([61.481], dtype=object), array([63.635], dtype=object)]
Predicted Values: [[59.61036476]
[62.45462785]
[63.88894641]]
Explained Variance Score: 0.8735550552790002
Maximam Error : 0.9736278468952051
Mean Absolute Error : 0.5227365013333625
Mean Squared Error : 0.37615744482145236
r2 score : 0.835279327791174
Origional Values: [array([114.402], dtype=object), array([120.849], dtype=object), array([135.202], dtype=object)]
Predicted Values: [[104.33048452]
[118.40765301]
[125.06518713]]
Explained Variance Score: 0.8273422731672979
Maximam Error : 10.136812867646142
Mean Absolute Error : 7.549891780685708
Mean Squared Error : 70.05019143953847
r2 score : 0.07315464070835842
Origional Values: [array([283.896], dtype=object), array([277.432], dtype=object), array([273.84], dtype=object)]
Predicted Values: [[299.0867623 ]
[298.69596981]
[298.43640521]]
Explained Variance Score: 0.12421500114060657
Maximam Error : 24.596405209985903
Mean Absolute Error : 20.35037910419315
Mean Squared Error : 429.2996067738971
r2 score : -23.797662778927894
Origional Values: [array([48.975], dtype=object), array([49.611], dtype=object), array([51.674], dtype=object)]
Predicted Values: [[49.59575225]
[44.75768134]
[45.41826809]]
Explained Variance Score: -5.631880456190142
Maximam Error : 6.255731908177069
Mean Absolute Error : 3.9099342751089097
Mean Squared Error : 21.024739037148265
r2 score : -14.84107206559884
Origional Values: [array([69.888], dtype=object), array([70.183], dtype=object), array([72.506], dtype=object)]
Predicted Values: [[84.13401102]
[78.51340174]
[78.67527617]]
Explained Variance Score: -7.502567820277013
Maximam Error : 14.246011023523721
Mean Absolute Error : 9.581896309660246
Mean Squared Error : 103.46813054603744
r2 score : -74.4796296974374
Origional Values: [array([1324.769], dtype=object), array([1278.977], dtype=object), array([1167.53], dtype=object)]
Predicted Values: [[1293.56910957]
[1290.65570575]
[1271.65946655]]
Explained Variance Score: 0.2686367019756183
Maximam Error : 104.12946655213887
Mean Absolute Error : 49.00268757638264
Mean Squared Error : 3984.2570450732983
r2 score : 0.08621320794451337
Origional Values: [array([502.969], dtype=object), array([569.349], dtype=object), array([595.054], dtype=object)]
Predicted Values: [[523.55489638]
[525.82797378]
[587.48995898]]
Explained Variance Score: 0.5426914375454851
Maximam Error : 43.52102622117616
Mean Absolute Error : 23.890321206413415
Mean Squared Error : 791.6911898711245
r2 score : 0.4740253036602301
Origional Values: [array([72.008], dtype=object), array([70.825], dtype=object), array([70.497], dtype=object)]
Predicted Values: [[75.12592457]
[74.17207521]
[73.07866069]]
Explained Variance Score: 0.7556989542820238
Maximam Error : 3.3470752137563835
Mean Absolute Error : 3.0155534913028674
Mean Squared Error : 9.19644600976158
r2 score : -20.83740834581418
Origional Values: [array([30.164], dtype=object), array([29.494], dtype=object), array([30.648], dtype=object)]
Predicted Values: [[34.72920785]
[34.34004711]
[34.2004659 ]]
Explained Variance Score: -0.3786827650384259
Maximam Error : 4.846047106578773
Mean Absolute Error : 4.321240285217784
Mean Squared Error : 18.981769747051136
r2 score : -83.78748412974133
Origional Values: [array([15.195], dtype=object), array([14.321], dtype=object), array([14.0], dtype=object)]
Predicted Values: [[17.27507464]
[17.08805754]
[16.85116921]]
Explained Variance Score: 0.532185515825517
Maximam Error : 2.8511692096809043
Mean Absolute Error : 2.56610046347239
Mean Squared Error : 6.704161267293254
r2 score : -25.291492946505507
Origional Values: [array([330.458], dtype=object), array([328.481], dtype=object), array([313.451], dtype=object)]
Predicted Values: [[317.74284012]
[318.53789322]
[318.20138712]]
Explained Variance Score: -0.018458611258797664
Maximam Error : 12.715159880990598
Mean Absolute Error : 9.136217927649605
Mean Squared Error : 94.36894700959806
r2 score : -0.6363060395919762
Origional Values: [array([277.159], dtype=object), array([279.68], dtype=object), array([293.817], dtype=object)]
Predicted Values: [[287.22088288]
[264.5705186 ]
[267.01399365]]
Explained Variance Score: -3.402232374298433
Maximam Error : 26.803006352855505
Mean Absolute Error : 17.324790209198415
Mean Squared Error : 349.31302156527227
r2 score : -5.499529796772914
Origional Values: [array([76.184], dtype=object), array([74.291], dtype=object), array([70.196], dtype=object)]
Predicted Values: [[81.89403785]
[81.94964296]
[82.06912138]]
Explained Variance Score: -0.05931244968225813
Maximam Error : 11.87312137880265
Mean Absolute Error : 8.41393406316197
Mean Squared Error : 77.41011851110777
r2 score : -11.394737522277635
Origional Values: [array([98.287], dtype=object), array([90.92], dtype=object), array([94.16], dtype=object)]
Predicted Values: [[97.43670196]
[97.6505935 ]
[93.92544219]]
Explained Variance Score: -0.30023522546501424
Maximam Error : 6.730593502382774
Mean Absolute Error : 2.6051497839552886
Mean Squared Error : 15.3596376730203
r2 score : -0.6898857324386489
Origional Values: [array([211.848], dtype=object), array([214.94], dtype=object), array([210.858], dtype=object)]
Predicted Values: [[212.93795319]
[213.78602857]
[215.6510706 ]]
Explained Variance Score: -0.9893030999999208
Maximam Error : 4.7930706011682105
Mean Absolute Error : 2.345665071861049
Mean Squared Error : 8.49772459764275
r2 score : -1.8114072599894944
Origional Values: [array([255.149], dtype=object), array([239.72], dtype=object), array([235.388], dtype=object)]
Predicted Values: [[258.58171896]
[264.48996863]
[248.4804175 ]]
Explained Variance Score: -0.05814120616968932
Maximam Error : 24.76996862973479
Mean Absolute Error : 13.765035031333326
Mean Squared Error : 265.58210048012813
r2 score : -2.6925300571505946
Origional Values: [array([26.991], dtype=object), array([28.711], dtype=object), array([29.138], dtype=object)]
Predicted Values: [[27.68352776]
[27.65266497]
[29.31153963]]
Explained Variance Score: 0.3739131980243
Maximam Error : 1.0583350267111875
Mean Absolute Error : 0.6414674707015836
Mean Squared Error : 0.5432612420609545
r2 score : 0.3691434904453734
Origional Values: [array([368.813], dtype=object), array([369.74], dtype=object), array([374.947], dtype=object)]
Predicted Values: [[347.49422066]
[352.20260788]
[352.56718146]]
Explained Variance Score: 0.4073949256923892
Maximam Error : 22.37981853914698
Mean Absolute Error : 20.411996668887962
Mean Squared Error : 420.9689176855277
r2 score : -56.75652489093273
Origional Values: [array([612.671], dtype=object), array([590.239], dtype=object), array([575.797], dtype=object)]
Predicted Values: [[621.4957409]
[612.7713929]
[595.3491037]]
Explained Variance Score: 0.8494499140859224
Maximam Error : 22.53239289625685
Mean Absolute Error : 16.96974583418728
Mean Squared Error : 322.6231802760934
r2 score : -0.4017222715203812
Origional Values: [array([5837.047], dtype=object), array([5854.868], dtype=object), array([5794.513], dtype=object)]
Predicted Values: [[5681.25996945]
[5773.54467202]
[5788.37791082]]
Explained Variance Score: -4.8227018869582885
Maximam Error : 155.78703054876223
Mean Absolute Error : 81.08181590264515
Mean Squared Error : 10306.907293166116
r2 score : -15.078149854560763
Origional Values: [array([123.209], dtype=object), array([131.967], dtype=object), array([152.455], dtype=object)]
Predicted Values: [[119.59774601]
[124.03294311]
[130.74861673]]
Explained Variance Score: 0.6036384272598692
Maximam Error : 21.706383272271154
Mean Absolute Error : 11.083898048789138
Mean Squared Error : 182.38582960459624
r2 score : -0.214296161929876
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [[31984.90600637]
[32520.7585233 ]
[32555.40789394]]
Explained Variance Score: -132.9562192489812
Maximam Error : 302.8939936349161
Mean Absolute Error : 258.4201369571929
Mean Squared Error : 69015.51142951909
r2 score : -139.45051710028866
i=0
VVIP2=list()
while i<len(CO2_FINAL_DF_FINAL.T):
series=CO2_FINAL_DF_FINAL[CO2_FINAL_DF_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = RandomForestRegressor()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP2.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([205.328], dtype=object), array([203.334], dtype=object), array([216.033], dtype=object)]
Predicted Values: [200.15275 200.15275 200.15275]
Explained Variance Score: 0.0
Maximam Error : 15.880250000000132
Mean Absolute Error : 8.078916666666808
Mean Squared Error : 96.36196806250221
r2 score : -2.099145791757373
Origional Values: [array([448.017], dtype=object), array([423.099], dtype=object), array([426.401], dtype=object)]
Predicted Values: [461.26374 453.08299 441.14518]
Explained Variance Score: 0.5317552955238753
Maximam Error : 29.98398999999972
Mean Absolute Error : 19.32496999999974
Mean Squared Error : 430.63554027335675
r2 score : -2.526390717463522
Origional Values: [array([86.533], dtype=object), array([82.95], dtype=object), array([83.435], dtype=object)]
Predicted Values: [89.78268 89.78268 89.78268]
Explained Variance Score: 0.0
Maximam Error : 6.832679999999982
Mean Absolute Error : 5.476679999999983
Mean Squared Error : 32.51299248906648
r2 score : -11.907263563580056
Origional Values: [array([117.656], dtype=object), array([112.33], dtype=object), array([117.828], dtype=object)]
Predicted Values: [119.52256 119.52256 119.52256]
Explained Variance Score: 0.0
Maximam Error : 7.192559999999943
Mean Absolute Error : 3.5845599999999394
Mean Squared Error : 19.362833060266247
r2 score : -1.9726034016180822
Origional Values: [array([542.574], dtype=object), array([555.957], dtype=object), array([475.385], dtype=object)]
Predicted Values: [493.46491 493.46491 493.46491]
Explained Variance Score: -2.220446049250313e-16
Maximam Error : 62.49208999999939
Mean Absolute Error : 43.227029999999786
Mean Squared Error : 2214.615726268061
r2 score : -0.7819385544187321
Origional Values: [array([6.635], dtype=object), array([5.438], dtype=object), array([6.39], dtype=object)]
Predicted Values: [5.33052 5.48304 6.04904]
Explained Variance Score: -0.20817186039319102
Maximam Error : 1.3044799999999963
Mean Absolute Error : 0.5634933333333322
Mean Squared Error : 0.6066501311999961
r2 score : -1.275755367469468
Origional Values: [array([32.037], dtype=object), array([33.911], dtype=object), array([34.763], dtype=object)]
Predicted Values: [38.60492 38.60492 38.60492]
Explained Variance Score: 0.0
Maximam Error : 6.567920000000008
Mean Absolute Error : 5.034586666666674
Mean Squared Error : 26.643602459733412
r2 score : -19.549779870246123
Origional Values: [array([9.679], dtype=object), array([10.972], dtype=object), array([12.595], dtype=object)]
Predicted Values: [8.85028 8.85028 8.85028]
Explained Variance Score: 0.0
Maximam Error : 3.7447200000000027
Mean Absolute Error : 2.2317200000000024
Mean Squared Error : 6.40380015840001
r2 score : -3.4994963262335057
Origional Values: [array([561.804], dtype=object), array([563.554], dtype=object), array([547.864], dtype=object)]
Predicted Values: [568.07331 564.25407 558.49834]
Explained Variance Score: 0.6646250705957383
Maximam Error : 10.634340000000975
Mean Absolute Error : 5.867906666667484
Mean Squared Error : 50.961177705542696
r2 score : -0.03401642283740092
Origional Values: [array([99.348], dtype=object), array([89.911], dtype=object), array([89.237], dtype=object)]
Predicted Values: [93.6457 93.6457 93.6457]
Explained Variance Score: 0.0
Maximam Error : 5.7022999999999655
Mean Absolute Error : 4.615233333333346
Mean Squared Error : 21.96694835666673
r2 score : -0.03107780529126325
Origional Values: [array([107.616], dtype=object), array([112.459], dtype=object), array([104.308], dtype=object)]
Predicted Values: [95.30773 95.30773 95.30773]
Explained Variance Score: 0.0
Maximam Error : 17.151269999999926
Mean Absolute Error : 12.819936666666592
Mean Squared Error : 175.55481102623142
r2 score : -14.668891856123224
Origional Values: [array([7782.052], dtype=object), array([7783.586], dtype=object), array([7977.936], dtype=object)]
Predicted Values: [7233.7791 7233.7791 7233.7791]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 744.15689999999
Mean Absolute Error : 614.0788999999901
Mean Squared Error : 385553.43065986456
r2 score : -44.570808461393504
Origional Values: [array([99.774], dtype=object), array([103.908], dtype=object), array([97.378], dtype=object)]
Predicted Values: [89.88981 89.88981 89.88981]
Explained Variance Score: 0.0
Maximam Error : 14.01818999999989
Mean Absolute Error : 10.463523333333223
Mean Squared Error : 116.75995076943104
r2 score : -15.050293582312655
Origional Values: [array([12.926], dtype=object), array([13.348], dtype=object), array([13.546], dtype=object)]
Predicted Values: [11.65258 11.65258 11.65258]
Explained Variance Score: 0.0
Maximam Error : 1.8934199999999777
Mean Absolute Error : 1.6207533333333117
Mean Squared Error : 2.693695589733263
r2 score : -39.292078797648294
Origional Values: [array([19.095], dtype=object), array([16.877], dtype=object), array([17.126], dtype=object)]
Predicted Values: [20.05372 20.05372 20.05372]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 3.1767199999999782
Mean Absolute Error : 2.354386666666644
Mean Squared Error : 6.527412798399893
r2 score : -5.631687986592639
Origional Values: [array([7.979], dtype=object), array([7.936], dtype=object), array([7.929], dtype=object)]
Predicted Values: [9.64631 9.64631 9.64631]
Explained Variance Score: 0.0
Maximam Error : 1.7173100000000137
Mean Absolute Error : 1.6983100000000138
Mean Squared Error : 2.8847455227667136
r2 score : -5902.299159822755
Origional Values: [array([95.808], dtype=object), array([93.444], dtype=object), array([91.848], dtype=object)]
Predicted Values: [108.79281 108.79281 108.79281]
Explained Variance Score: 0.0
Maximam Error : 16.94481000000006
Mean Absolute Error : 15.092810000000057
Mean Squared Error : 230.43928169610172
r2 score : -86.0775650612843
Origional Values: [array([66.926], dtype=object), array([62.631], dtype=object), array([59.383], dtype=object)]
Predicted Values: [67.67072 67.67072 67.67072]
Explained Variance Score: 0.0
Maximam Error : 8.287719999999986
Mean Absolute Error : 4.690719999999987
Mean Squared Error : 31.546562785066545
r2 score : -2.3054825840660147
Origional Values: [array([15.476], dtype=object), array([14.749], dtype=object), array([13.291], dtype=object)]
Predicted Values: [14.34537 16.774 14.49458]
Explained Variance Score: -1.1648016249539772
Maximam Error : 2.024999999999965
Mean Absolute Error : 1.4530699999999868
Mean Squared Error : 2.2758513377666145
r2 score : -1.7573012598070736
Origional Values: [array([58.446], dtype=object), array([55.964], dtype=object), array([52.286], dtype=object)]
Predicted Values: [62.682 62.682 62.682]
Explained Variance Score: 0.0
Maximam Error : 10.395999999999916
Mean Absolute Error : 7.1166666666665845
Mean Squared Error : 57.05067866666548
r2 score : -7.908970404906629
Origional Values: [array([475.829], dtype=object), array([448.16], dtype=object), array([445.014], dtype=object)]
Predicted Values: [485.92909 485.92909 485.92909]
Explained Variance Score: 0.0
Maximam Error : 40.915090000000305
Mean Absolute Error : 29.594756666666967
Mean Squared Error : 1067.5201890481178
r2 score : -4.569557216716316
Origional Values: [array([26760.591], dtype=object), array([26772.893], dtype=object), array([26687.974], dtype=object)]
Predicted Values: [26150.44091 26150.44091 26150.44091]
Explained Variance Score: 0.0
Maximam Error : 622.452090000017
Mean Absolute Error : 590.0450900000166
Mean Squared Error : 349557.18650579447
r2 score : -247.9762080447599
Origional Values: [array([898.539], dtype=object), array([864.752], dtype=object), array([853.444], dtype=object)]
Predicted Values: [911.79165 891.89304 911.79165]
Explained Variance Score: 0.03109071156851073
Maximam Error : 58.347650000000726
Mean Absolute Error : 32.91378000000062
Mean Squared Error : 1438.905681608909
r2 score : -2.9207343050376804
Origional Values: [array([78.272], dtype=object), array([78.306], dtype=object), array([72.95], dtype=object)]
Predicted Values: [93.398 93.398 93.398]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 20.448000000000178
Mean Absolute Error : 16.888666666666847
Mean Squared Error : 291.5616813333393
r2 score : -45.02670748831878
Origional Values: [array([46.072], dtype=object), array([46.181], dtype=object), array([48.26], dtype=object)]
Predicted Values: [50.65805 50.65805 50.65805]
Explained Variance Score: 0.0
Maximam Error : 4.586050000000043
Mean Absolute Error : 3.8203833333333796
Mean Squared Error : 15.608825035833682
r2 score : -14.40097012064765
Origional Values: [array([2.547], dtype=object), array([2.716], dtype=object), array([2.881], dtype=object)]
Predicted Values: [2.58635 2.59755 2.58635]
Explained Variance Score: -0.0009637747845732125
Maximam Error : 0.29464999999999586
Mean Absolute Error : 0.15081666666666468
Mean Squared Error : 0.03413248249999884
r2 score : -0.8357157348423601
Origional Values: [array([1696.941], dtype=object), array([1858.186], dtype=object), array([1918.813], dtype=object)]
Predicted Values: [1674.03025 1674.03025 1674.03025]
Explained Variance Score: 0.0
Maximam Error : 244.78274999999985
Mean Absolute Error : 150.61641666666642
Mean Squared Error : 31452.279140395764
r2 score : -2.587586609395336
Origional Values: [array([475.289], dtype=object), array([496.938], dtype=object), array([484.588], dtype=object)]
Predicted Values: [455.31025 455.31025 455.31025]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 41.62774999999937
Mean Absolute Error : 30.29474999999938
Mean Squared Error : 996.4022222291288
r2 score : -11.671980854886527
Origional Values: [array([47.516], dtype=object), array([48.607], dtype=object), array([46.672], dtype=object)]
Predicted Values: [50.10816 50.10816 50.10816]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 3.4361600000000436
Mean Absolute Error : 2.5098266666667093
Mean Squared Error : 6.9266567856002155
r2 score : -10.039783133723555
Origional Values: [array([90.278], dtype=object), array([89.165], dtype=object), array([88.27], dtype=object)]
Predicted Values: [97.22132 87.10632 86.95549]
Explained Variance Score: -23.66815747301612
Maximam Error : 6.943319999999915
Mean Absolute Error : 3.4388366666665795
Mean Squared Error : 18.058597501632743
r2 score : -25.767321883135804
Origional Values: [array([436.164], dtype=object), array([418.783], dtype=object), array([423.012], dtype=object)]
Predicted Values: [512.01389 512.01389 512.01389]
Explained Variance Score: 0.0
Maximam Error : 93.23089000000016
Mean Absolute Error : 86.02755666666684
Mean Squared Error : 7455.51369559213
r2 score : -135.1161136696293
Origional Values: [array([1497.445], dtype=object), array([1448.543], dtype=object), array([1361.019], dtype=object)]
Predicted Values: [1480.87309 1480.87309 1441.56621]
Explained Variance Score: 0.506403002784128
Maximam Error : 80.5472100000004
Mean Absolute Error : 43.14973666666682
Mean Squared Error : 2602.5719864134667
r2 score : 0.18283485543926015
Origional Values: [array([181.262], dtype=object), array([163.473], dtype=object), array([180.186], dtype=object)]
Predicted Values: [159.47543 159.47543 159.47543]
Explained Variance Score: 0.0
Maximam Error : 21.786569999999813
Mean Absolute Error : 15.498236666666486
Mean Squared Error : 306.520969331561
r2 score : -3.621455859304189
Origional Values: [array([582.966], dtype=object), array([576.605], dtype=object), array([584.818], dtype=object)]
Predicted Values: [595.1753 599.48922 589.07653]
Explained Variance Score: -3.7068318534039992
Maximam Error : 22.884219999998777
Mean Absolute Error : 13.117349999998888
Mean Squared Error : 230.29653641973587
r2 score : -17.614735916516263
Origional Values: [array([9.864], dtype=object), array([9.377], dtype=object), array([9.229], dtype=object)]
Predicted Values: [11.46313 11.46313 11.46313]
Explained Variance Score: 0.0
Maximam Error : 2.234129999999995
Mean Absolute Error : 1.973129999999994
Mean Squared Error : 3.9668306635666433
r2 score : -52.90545641403062
Origional Values: [array([14.109], dtype=object), array([13.952], dtype=object), array([14.205], dtype=object)]
Predicted Values: [15.66478 15.66478 15.66478]
Explained Variance Score: 0.0
Maximam Error : 1.7127800000000217
Mean Absolute Error : 1.576113333333355
Mean Squared Error : 2.4950081284000682
r2 score : -228.42837889123362
Origional Values: [array([10.014], dtype=object), array([9.232], dtype=object), array([9.147], dtype=object)]
Predicted Values: [10.57308 10.37818 10.37818]
Explained Variance Score: 0.4135963684602906
Maximam Error : 1.2311800000000002
Mean Absolute Error : 0.9788133333333319
Mean Squared Error : 1.0473677437333322
r2 score : -5.878318970723229
Origional Values: [array([201.847], dtype=object), array([210.804], dtype=object), array([209.462], dtype=object)]
Predicted Values: [176.57566 176.57566 176.57566]
Explained Variance Score: 0.0
Maximam Error : 34.2283400000002
Mean Absolute Error : 30.7953400000002
Mean Squared Error : 963.9104143822789
r2 score : -60.95812919168118
Origional Values: [array([2.979], dtype=object), array([2.846], dtype=object), array([2.597], dtype=object)]
Predicted Values: [3.16692 2.98566 3.01299]
Explained Variance Score: 0.42067769996542537
Maximam Error : 0.4159900000000003
Mean Absolute Error : 0.2478566666666667
Mean Squared Error : 0.07595550736666674
r2 score : -2.029951892613048
Origional Values: [array([492.039], dtype=object), array([486.441], dtype=object), array([485.486], dtype=object)]
Predicted Values: [483.38275 483.38275 460.7755 ]
Explained Variance Score: -9.079558222107591
Maximam Error : 24.710499999999513
Mean Absolute Error : 12.141666666666234
Mean Squared Error : 231.63078912498884
r2 score : -26.724926634930128
Origional Values: [array([61.914], dtype=object), array([63.151], dtype=object), array([66.593], dtype=object)]
Predicted Values: [60.95818 60.95818 60.95818]
Explained Variance Score: 0.0
Maximam Error : 5.63482000000004
Mean Absolute Error : 2.9278200000000396
Mean Squared Error : 12.491082619066901
r2 score : -2.1873522549307545
Origional Values: [array([178.633], dtype=object), array([168.994], dtype=object), array([179.238], dtype=object)]
Predicted Values: [186.44066 186.44066 186.44066]
Explained Variance Score: 0.0
Maximam Error : 17.446659999999923
Mean Absolute Error : 10.818993333333253
Mean Squared Error : 139.0746036355983
r2 score : -5.314687905383805
Origional Values: [array([43.508], dtype=object), array([43.891], dtype=object), array([42.83], dtype=object)]
Predicted Values: [41.50983 43.16686 43.16686]
Explained Variance Score: -3.7348692055973354
Maximam Error : 1.9981700000000089
Mean Absolute Error : 1.0197233333333362
Mean Squared Error : 1.5435122493666835
r2 score : -7.020124914871904
Origional Values: [array([63.328], dtype=object), array([63.259], dtype=object), array([59.565], dtype=object)]
Predicted Values: [63.97815 65.57365 65.57365]
Explained Variance Score: -0.6227544056457852
Maximam Error : 6.008650000000131
Mean Absolute Error : 2.9911500000000593
Mean Squared Error : 13.961391489167355
r2 score : -3.5181577175561944
Origional Values: [array([59.951], dtype=object), array([61.481], dtype=object), array([63.635], dtype=object)]
Predicted Values: [54.94803 54.94803 54.94803]
Explained Variance Score: 0.0
Maximam Error : 8.686969999999938
Mean Absolute Error : 6.74096999999994
Mean Squared Error : 47.72428454089919
r2 score : -19.89863257656273
Origional Values: [array([114.402], dtype=object), array([120.849], dtype=object), array([135.202], dtype=object)]
Predicted Values: [99.51291 99.51291 99.51291]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 35.68908999999982
Mean Absolute Error : 23.97142333333316
Mean Squared Error : 650.2082941814249
r2 score : -7.6030106078316635
Origional Values: [array([283.896], dtype=object), array([277.432], dtype=object), array([273.84], dtype=object)]
Predicted Values: [309.11886 298.87553 292.12593]
Explained Variance Score: 0.5354901248141751
Maximam Error : 25.222860000000423
Mean Absolute Error : 21.65077333333369
Mean Squared Error : 476.79762713514884
r2 score : -26.541294203228997
Origional Values: [array([48.975], dtype=object), array([49.611], dtype=object), array([51.674], dtype=object)]
Predicted Values: [55.78484 55.78484 55.78484]
Explained Variance Score: 0.0
Maximam Error : 6.809839999999902
Mean Absolute Error : 5.698173333333237
Mean Squared Error : 33.79640889226557
r2 score : -24.46387604977572
Origional Values: [array([69.888], dtype=object), array([70.183], dtype=object), array([72.506], dtype=object)]
Predicted Values: [83.25616 83.25616 83.25616]
Explained Variance Score: 0.0
Maximam Error : 13.368160000000032
Mean Absolute Error : 12.397160000000033
Mean Squared Error : 155.06038473226747
r2 score : -112.11599386756247
Origional Values: [array([1324.769], dtype=object), array([1278.977], dtype=object), array([1167.53], dtype=object)]
Predicted Values: [1275.84838 1275.84838 1197.3529 ]
Explained Variance Score: 0.7608841348979841
Maximam Error : 48.92061999999942
Mean Absolute Error : 27.290713333333162
Mean Squared Error : 1097.4735628995911
r2 score : 0.7482951438467562
Origional Values: [array([502.969], dtype=object), array([569.349], dtype=object), array([595.054], dtype=object)]
Predicted Values: [484.50298 484.50298 484.50298]
Explained Variance Score: 0.0
Maximam Error : 110.55102000000011
Mean Absolute Error : 71.2876866666668
Mean Squared Error : 6587.1230091737525
r2 score : -3.376277099997628
Origional Values: [array([72.008], dtype=object), array([70.825], dtype=object), array([70.497], dtype=object)]
Predicted Values: [70.49889 70.49889 70.49889]
Explained Variance Score: 0.0
Maximam Error : 1.509110000000021
Mean Absolute Error : 0.612370000000008
Mean Squared Error : 0.7945880987666939
r2 score : -0.8867880876019267
Origional Values: [array([30.164], dtype=object), array([29.494], dtype=object), array([30.648], dtype=object)]
Predicted Values: [34.32075 34.32075 34.32075]
Explained Variance Score: 0.0
Maximam Error : 4.826749999999969
Mean Absolute Error : 4.218749999999968
Mean Squared Error : 18.021726229166397
r2 score : -79.49917615734277
Origional Values: [array([15.195], dtype=object), array([14.321], dtype=object), array([14.0], dtype=object)]
Predicted Values: [17.17476 17.17476 17.17476]
Explained Variance Score: 0.0
Maximam Error : 3.1747600000000133
Mean Absolute Error : 2.66942666666668
Mean Squared Error : 7.380832284266738
r2 score : -27.945171842425907
Origional Values: [array([330.458], dtype=object), array([328.481], dtype=object), array([313.451], dtype=object)]
Predicted Values: [315.07176 315.07176 315.07176]
Explained Variance Score: 0.0
Maximam Error : 15.386239999999646
Mean Absolute Error : 10.13874666666654
Mean Squared Error : 139.7236538975933
r2 score : -1.4227319048419322
Origional Values: [array([277.159], dtype=object), array([279.68], dtype=object), array([293.817], dtype=object)]
Predicted Values: [309.38435 309.38435 309.38435]
Explained Variance Score: 0.0
Maximam Error : 32.22534999999954
Mean Absolute Error : 25.832349999999526
Mean Squared Error : 721.0546591891424
r2 score : -12.41638001784612
Origional Values: [array([76.184], dtype=object), array([74.291], dtype=object), array([70.196], dtype=object)]
Predicted Values: [80.76352 80.76352 80.76352]
Explained Variance Score: 0.0
Maximam Error : 10.567519999999988
Mean Absolute Error : 7.206519999999988
Mean Squared Error : 58.17933251039983
r2 score : -8.315546462885791
Origional Values: [array([98.287], dtype=object), array([90.92], dtype=object), array([94.16], dtype=object)]
Predicted Values: [97.37921 97.40557 94.30818]
Explained Variance Score: -0.17281810352597682
Maximam Error : 6.485569999999953
Mean Absolute Error : 2.513846666666685
Mean Squared Error : 14.302886073799838
r2 score : -0.5736206558612775
Origional Values: [array([211.848], dtype=object), array([214.94], dtype=object), array([210.858], dtype=object)]
Predicted Values: [218.32003 218.32003 218.11248]
Explained Variance Score: 0.07422754172211321
Maximam Error : 7.2544800000001715
Mean Absolute Error : 5.702180000000074
Mean Squared Error : 35.31308506406765
r2 score : -10.683064399296525
Origional Values: [array([255.149], dtype=object), array([239.72], dtype=object), array([235.388], dtype=object)]
Predicted Values: [238.75499 238.75499 238.75499]
Explained Variance Score: 0.0
Maximam Error : 16.394009999999554
Mean Absolute Error : 6.908669999999849
Mean Squared Error : 93.67714328009583
r2 score : -0.3024434500834845
Origional Values: [array([26.991], dtype=object), array([28.711], dtype=object), array([29.138], dtype=object)]
Predicted Values: [26.51741 26.51741 26.51741]
Explained Variance Score: 0.0
Maximam Error : 2.6205899999999964
Mean Absolute Error : 1.7625899999999948
Mean Squared Error : 3.9678721747666486
r2 score : -3.607650604773598
Origional Values: [array([368.813], dtype=object), array([369.74], dtype=object), array([374.947], dtype=object)]
Predicted Values: [350.32522 350.32522 350.32522]
Explained Variance Score: 0.0
Maximam Error : 24.62177999999966
Mean Absolute Error : 20.841446666666325
Mean Squared Error : 441.65458071505236
r2 score : -59.59457768166793
Origional Values: [array([612.671], dtype=object), array([590.239], dtype=object), array([575.797], dtype=object)]
Predicted Values: [633.66782 635.90291 626.59235]
Explained Variance Score: 0.2648899974425639
Maximam Error : 50.79535000000044
Mean Absolute Error : 39.15202666666661
Mean Squared Error : 1702.0755694076688
r2 score : -6.395120311589886
Origional Values: [array([5837.047], dtype=object), array([5854.868], dtype=object), array([5794.513], dtype=object)]
Predicted Values: [6014.63618 6014.63618 6014.63618]
Explained Variance Score: 0.0
Maximam Error : 220.1231800000014
Mean Absolute Error : 185.82684666666805
Mean Squared Error : 35172.66752229958
r2 score : -53.867226717284176
Origional Values: [array([123.209], dtype=object), array([131.967], dtype=object), array([152.455], dtype=object)]
Predicted Values: [118.93583 119.17221 119.17221]
Explained Variance Score: 0.0132084349499062
Maximam Error : 33.282790000000205
Mean Absolute Error : 16.783583333333482
Mean Squared Error : 429.9035810590397
r2 score : -1.862230413468184
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [31456.338 31456.338 31456.338]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 871.4619999999959
Mean Absolute Error : 840.8619999999961
Mean Squared Error : 707540.2897106601
r2 score : -1438.8849983258428
i=0
VVIP3=list()
VVIP4=list()
while i<len(CO2_FINAL_DF_FINAL.T):
series=CO2_FINAL_DF_FINAL[CO2_FINAL_DF_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = MLPRegressor ()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP3.append(pre)
VVIP4.append(y_test)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([205.328], dtype=object), array([203.334], dtype=object), array([216.033], dtype=object)]
Predicted Values: [215.56700541 217.83381547 215.72165694]
Explained Variance Score: -0.2465612019558483
Maximam Error : 14.499815469633205
Mean Absolute Error : 8.350054647418185
Mean Squared Error : 105.0596049859563
r2 score : -2.3788748737959695
Origional Values: [array([448.017], dtype=object), array([423.099], dtype=object), array([426.401], dtype=object)]
Predicted Values: [481.22675449 459.36827336 433.86159392]
Explained Variance Score: -0.36691176569001227
Maximam Error : 36.269273364554635
Mean Absolute Error : 25.64654059400853
Mean Squared Error : 824.6694818851441
r2 score : -5.753058059372504
Origional Values: [array([86.533], dtype=object), array([82.95], dtype=object), array([83.435], dtype=object)]
Predicted Values: [85.3996316 85.73620103 82.21009496]
Explained Variance Score: -0.3877097063294528
Maximam Error : 2.786201033882861
Mean Absolute Error : 1.7148248237294628
Mean Squared Error : 3.5159441606633632
r2 score : -0.39578717559674526
Origional Values: [array([117.656], dtype=object), array([112.33], dtype=object), array([117.828], dtype=object)]
Predicted Values: [117.26038918 116.98555764 111.72025984]
Explained Variance Score: -1.9679262333356422
Maximam Error : 6.107740160008547
Mean Absolute Error : 3.7196362063116055
Mean Squared Error : 19.711738233957973
r2 score : -2.026167707157982
Origional Values: [array([542.574], dtype=object), array([555.957], dtype=object), array([475.385], dtype=object)]
Predicted Values: [547.84638085 582.10580262 596.44000638]
Explained Variance Score: -1.042734759429882
Maximam Error : 121.05500638193894
Mean Absolute Error : 50.82539661712766
Mean Squared Error : 5121.95748279804
r2 score : -3.1212628468378343
Origional Values: [array([6.635], dtype=object), array([5.438], dtype=object), array([6.39], dtype=object)]
Predicted Values: [6.31935121 6.7867617 5.71494111]
Explained Variance Score: -1.9157504322342538
Maximam Error : 1.3487616953866235
Mean Absolute Error : 0.779823125776525
Mean Squared Error : 0.7914989263369718
r2 score : -1.9691874069072917
Origional Values: [array([32.037], dtype=object), array([33.911], dtype=object), array([34.763], dtype=object)]
Predicted Values: [36.08078286 32.14383414 33.9746842 ]
Explained Variance Score: -3.9768760609458615
Maximam Error : 4.043782864939956
Mean Absolute Error : 2.1997548435352265
Mean Squared Error : 6.698832282753956
r2 score : -4.166701049767484
Origional Values: [array([9.679], dtype=object), array([10.972], dtype=object), array([12.595], dtype=object)]
Predicted Values: [ 9.65014052 10.21862632 11.51273219]
Explained Variance Score: 0.8639428017211536
Maximam Error : 1.0822678052192956
Mean Absolute Error : 0.6215003209932762
Mean Squared Error : 0.579902791072355
r2 score : 0.5925434252379068
Origional Values: [array([561.804], dtype=object), array([563.554], dtype=object), array([547.864], dtype=object)]
Predicted Values: [563.07055389 567.05020387 568.81468005]
Explained Variance Score: -0.5715831968923515
Maximam Error : 20.95068004897928
Mean Absolute Error : 8.571145937031133
Mean Squared Error : 150.91953159572964
r2 score : -2.0621991332029133
Origional Values: [array([99.348], dtype=object), array([89.911], dtype=object), array([89.237], dtype=object)]
Predicted Values: [102.21139407 105.82129494 95.83107955]
Explained Variance Score: -0.41298876945036644
Maximam Error : 15.910294937375113
Mean Absolute Error : 8.45592285518901
Mean Squared Error : 101.60613192983733
r2 score : -3.769157090614116
Origional Values: [array([107.616], dtype=object), array([112.459], dtype=object), array([104.308], dtype=object)]
Predicted Values: [103.98335232 113.1624363 118.21800393]
Explained Variance Score: -3.9680483009760543
Maximam Error : 13.910003932117661
Mean Absolute Error : 6.08202930404768
Mean Squared Error : 69.05972039422424
r2 score : -5.1638258965715345
Origional Values: [array([7782.052], dtype=object), array([7783.586], dtype=object), array([7977.936], dtype=object)]
Predicted Values: [8022.3585044 8468.4142069 8470.08335633]
Explained Variance Score: -2.9155556502007136
Maximam Error : 684.828206903002
Mean Absolute Error : 472.4273558778289
Mean Squared Error : 256315.30312353803
r2 score : -29.29540046985422
Origional Values: [array([99.774], dtype=object), array([103.908], dtype=object), array([97.378], dtype=object)]
Predicted Values: [ 97.64640582 105.61703025 109.96636517]
Explained Variance Score: -4.340310763652383
Maximam Error : 12.588365167977571
Mean Absolute Error : 5.4749965301909596
Mean Squared Error : 55.30479298790829
r2 score : -6.602419820455701
Origional Values: [array([12.926], dtype=object), array([13.348], dtype=object), array([13.546], dtype=object)]
Predicted Values: [13.03512532 13.56131885 13.98589565]
Explained Variance Score: 0.7147984445635254
Maximam Error : 0.43989565131137454
Mean Absolute Error : 0.25411327179957627
Mean Squared Error : 0.0836404830699588
r2 score : -0.2510875198269378
Origional Values: [array([19.095], dtype=object), array([16.877], dtype=object), array([17.126], dtype=object)]
Predicted Values: [18.72347447 18.80015846 16.70034268]
Explained Variance Score: -0.21752638132896474
Maximam Error : 1.9231584587043713
Mean Absolute Error : 0.9067804349405565
Mean Squared Error : 1.3392512759749025
r2 score : -0.3606457676598611
Origional Values: [array([7.979], dtype=object), array([7.936], dtype=object), array([7.929], dtype=object)]
Predicted Values: [9.10215 7.94471742 7.90382212]
Explained Variance Score: -581.4842640919936
Maximam Error : 1.1231500006684572
Mean Absolute Error : 0.38568176561163625
Mean Squared Error : 0.4207252809847997
r2 score : -859.9657864627571
Origional Values: [array([95.808], dtype=object), array([93.444], dtype=object), array([91.848], dtype=object)]
Predicted Values: [99.43497098 96.36019656 93.99483154]
Explained Variance Score: 0.8619518620508313
Maximam Error : 3.6269709806160364
Mean Absolute Error : 2.896666361978594
Mean Squared Error : 8.756002187346569
r2 score : -2.3086865422142857
Origional Values: [array([66.926], dtype=object), array([62.631], dtype=object), array([59.383], dtype=object)]
Predicted Values: [63.7381317 65.94127989 61.74875819]
Explained Variance Score: 0.13892292891766433
Maximam Error : 3.310279892506472
Mean Absolute Error : 2.9546354601016156
Mean Squared Error : 8.905756356195383
r2 score : 0.0668453253083322
Origional Values: [array([15.476], dtype=object), array([14.749], dtype=object), array([13.291], dtype=object)]
Predicted Values: [13.99277392 15.3339243 14.63145 ]
Explained Variance Score: -0.7259447591724015
Maximam Error : 1.4832260799542443
Mean Absolute Error : 1.1362001282252894
Mean Squared Error : 1.4463007514647552
r2 score : -0.7522615901560434
Origional Values: [array([58.446], dtype=object), array([55.964], dtype=object), array([52.286], dtype=object)]
Predicted Values: [57.53969341 57.74054537 55.32057179]
Explained Variance Score: 0.5781827937228889
Maximam Error : 3.0345717915115102
Mean Absolute Error : 1.9058079160963288
Mean Squared Error : 4.395377011655326
r2 score : 0.3136228239450499
Origional Values: [array([475.829], dtype=object), array([448.16], dtype=object), array([445.014], dtype=object)]
Predicted Values: [470.02834171 470.94180584 443.61787184]
Explained Variance Score: 0.17628943764074945
Maximam Error : 22.781805840774496
Mean Absolute Error : 9.992864097224412
Mean Squared Error : 184.86916259340765
r2 score : 0.035484865547582456
Origional Values: [array([26760.591], dtype=object), array([26772.893], dtype=object), array([26687.974], dtype=object)]
Predicted Values: [27150.00754148 27549.19795709 27561.86217171]
Explained Variance Score: -30.174737773759247
Maximam Error : 873.8881717072873
Mean Absolute Error : 679.869890092507
Mean Squared Error : 505991.72194483835
r2 score : -359.39854162682497
Origional Values: [array([898.539], dtype=object), array([864.752], dtype=object), array([853.444], dtype=object)]
Predicted Values: [879.09543563 897.61670009 863.88203905]
Explained Variance Score: -0.25099328335299975
Maximam Error : 32.86470008890501
Mean Absolute Error : 20.915434503491195
Mean Squared Error : 522.3644555167008
r2 score : -0.42334015818648596
Origional Values: [array([78.272], dtype=object), array([78.306], dtype=object), array([72.95], dtype=object)]
Predicted Values: [86.42189859 75.98552351 76.01818822]
Explained Variance Score: -1.8852077697425367
Maximam Error : 8.149898591760731
Mean Absolute Error : 4.51285443416819
Mean Squared Error : 27.07307905106387
r2 score : -3.2738287301437685
Origional Values: [array([46.072], dtype=object), array([46.181], dtype=object), array([48.26], dtype=object)]
Predicted Values: [46.1525345 44.31799181 44.4214399 ]
Explained Variance Score: -1.5258512152050994
Maximam Error : 3.8385601006554353
Mean Absolute Error : 1.927367594800084
Mean Squared Error : 6.070609653111386
r2 score : -4.989770380989479
Origional Values: [array([2.547], dtype=object), array([2.716], dtype=object), array([2.881], dtype=object)]
Predicted Values: [2.59373352 2.56814613 2.70763869]
Explained Variance Score: 0.4803658580265773
Maximam Error : 0.17336130695776086
Mean Absolute Error : 0.12264956683902477
Mean Squared Error : 0.018032977407203814
r2 score : 0.03014905603593443
Origional Values: [array([1696.941], dtype=object), array([1858.186], dtype=object), array([1918.813], dtype=object)]
Predicted Values: [1874.42324903 1833.94558719 2008.12883782]
Explained Variance Score: 0.2223293004542123
Maximam Error : 177.4822490337467
Mean Absolute Error : 97.01283322357176
Mean Squared Error : 13354.955073835547
r2 score : -0.5233254727932519
Origional Values: [array([475.289], dtype=object), array([496.938], dtype=object), array([484.588], dtype=object)]
Predicted Values: [502.87135005 508.08965567 531.18740631]
Explained Variance Score: -1.6681234134985683
Maximam Error : 46.59940631195269
Mean Absolute Error : 28.444470675013367
Mean Squared Error : 1018.883375598291
r2 score : -11.95789023840081
Origional Values: [array([47.516], dtype=object), array([48.607], dtype=object), array([46.672], dtype=object)]
Predicted Values: [47.44965612 46.42098179 47.46886572]
Explained Variance Score: -1.5033012565385002
Maximam Error : 2.1860182131608
Mean Absolute Error : 1.016409272621516
Mean Squared Error : 1.8060240400097654
r2 score : -1.8784613346872194
Origional Values: [array([90.278], dtype=object), array([89.165], dtype=object), array([88.27], dtype=object)]
Predicted Values: [102.01134664 93.20204664 92.06262178]
Explained Variance Score: -19.149991749799007
Maximam Error : 11.733346643372201
Mean Absolute Error : 6.521005021179202
Mean Squared Error : 56.11771633135016
r2 score : -82.18037855663732
Origional Values: [array([436.164], dtype=object), array([418.783], dtype=object), array([423.012], dtype=object)]
Predicted Values: [453.06142583 425.04292303 408.11887101]
Explained Variance Score: -2.187381483283472
Maximam Error : 16.89742582763921
Mean Absolute Error : 12.683492618052165
Mean Squared Error : 182.17164239556038
r2 score : -2.325927225961292
Origional Values: [array([1497.445], dtype=object), array([1448.543], dtype=object), array([1361.019], dtype=object)]
Predicted Values: [1550.54364466 1516.71444549 1467.21516368]
Explained Variance Score: 0.8432727741083033
Maximam Error : 106.19616367769117
Mean Absolute Error : 75.82208461118792
Mean Squared Error : 6248.145741852262
r2 score : -0.9618158287384753
Origional Values: [array([181.262], dtype=object), array([163.473], dtype=object), array([180.186], dtype=object)]
Predicted Values: [175.39760688 194.15498916 175.23380552]
Explained Variance Score: -3.3661104315310597
Maximam Error : 30.681989158417252
Mean Absolute Error : 13.832858920075134
Mean Squared Error : 333.43326519207864
r2 score : -4.027215986133824
Origional Values: [array([582.966], dtype=object), array([576.605], dtype=object), array([584.818], dtype=object)]
Predicted Values: [591.47226669 590.39300189 583.95888439]
Explained Variance Score: -1.9650491933527778
Maximam Error : 13.788001892818556
Mean Absolute Error : 7.717794732178921
Mean Squared Error : 87.73454961499505
r2 score : -6.091532930660561
Origional Values: [array([9.864], dtype=object), array([9.377], dtype=object), array([9.229], dtype=object)]
Predicted Values: [9.98741846 9.80689688 9.35835604]
Explained Variance Score: 0.7217429591526063
Maximam Error : 0.4298968846173281
Mean Absolute Error : 0.227557126708005
Mean Squared Error : 0.07225881054217502
r2 score : 0.018071480089668523
Origional Values: [array([14.109], dtype=object), array([13.952], dtype=object), array([14.205], dtype=object)]
Predicted Values: [14.39542765 14.00264385 13.85511501]
Explained Variance Score: -5.343984394190009
Maximam Error : 0.3498849948825029
Mean Absolute Error : 0.22898549587652717
Mean Squared Error : 0.06900836827043645
r2 score : -5.345661916688064
Origional Values: [array([10.014], dtype=object), array([9.232], dtype=object), array([9.147], dtype=object)]
Predicted Values: [10.36267582 9.9821565 9.26165744]
Explained Variance Score: 0.5477282540774774
Maximam Error : 0.7501564958003275
Mean Absolute Error : 0.4044965845193804
Mean Squared Error : 0.2324853076587964
r2 score : -0.5267876174837316
Origional Values: [array([201.847], dtype=object), array([210.804], dtype=object), array([209.462], dtype=object)]
Predicted Values: [193.04947349 216.05571275 225.64334059]
Explained Variance Score: -5.719051507523775
Maximam Error : 16.18134059032613
Mean Absolute Error : 10.07685995173363
Mean Squared Error : 122.27091428776117
r2 score : -6.8593165825280025
Origional Values: [array([2.979], dtype=object), array([2.846], dtype=object), array([2.597], dtype=object)]
Predicted Values: [3.09031451 3.03406453 2.93562708]
Explained Variance Score: 0.6443897515373709
Maximam Error : 0.33862707743126386
Mean Absolute Error : 0.21266870600206556
Mean Squared Error : 0.05414249524555385
r2 score : -1.1598059393919895
Origional Values: [array([492.039], dtype=object), array([486.441], dtype=object), array([485.486], dtype=object)]
Predicted Values: [507.13568917 502.65613474 496.94523843]
Explained Variance Score: 0.5065858682124856
Maximam Error : 16.215134744885006
Mean Absolute Error : 14.257020784735724
Mean Squared Error : 207.38492144512256
r2 score : -23.82283013401208
Origional Values: [array([61.914], dtype=object), array([63.151], dtype=object), array([66.593], dtype=object)]
Predicted Values: [64.47316496 64.66679375 65.94764302]
Explained Variance Score: 0.5455657510846474
Maximam Error : 2.5591649556616574
Mean Absolute Error : 1.573438561407606
Mean Squared Error : 3.087813861931822
r2 score : 0.21208186865951262
Origional Values: [array([178.633], dtype=object), array([168.994], dtype=object), array([179.238], dtype=object)]
Predicted Values: [175.62886506 176.5194058 167.01340117]
Explained Variance Score: -1.956124705104806
Maximam Error : 12.224598834584327
Mean Absolute Error : 7.584713191275426
Mean Squared Error : 71.69912528653578
r2 score : -2.2555016332082887
Origional Values: [array([43.508], dtype=object), array([43.891], dtype=object), array([42.83], dtype=object)]
Predicted Values: [41.27081313 43.22137896 43.59509918]
Explained Variance Score: -6.811011529113035
Maximam Error : 2.2371868675103954
Mean Absolute Error : 1.2239690278268494
Mean Squared Error : 2.012924722112032
r2 score : -9.45920284869543
Origional Values: [array([63.328], dtype=object), array([63.259], dtype=object), array([59.565], dtype=object)]
Predicted Values: [62.43778029 64.28365141 64.21443124]
Explained Variance Score: -0.7077499592464962
Maximam Error : 4.649431242131726
Mean Absolute Error : 2.1881007880362042
Mean Squared Error : 7.819870840901416
r2 score : -1.5306510327086684
Origional Values: [array([59.951], dtype=object), array([61.481], dtype=object), array([63.635], dtype=object)]
Predicted Values: [62.3874927 65.71328203 67.39042695]
Explained Variance Score: 0.7473862183070781
Maximam Error : 4.23228203397408
Mean Absolute Error : 3.4747338949892046
Mean Squared Error : 12.650646493771056
r2 score : -4.539762732382735
Origional Values: [array([114.402], dtype=object), array([120.849], dtype=object), array([135.202], dtype=object)]
Predicted Values: [104.86723095 118.97460289 125.64642117]
Explained Variance Score: 0.8269918621858021
Maximam Error : 9.555578827727373
Mean Absolute Error : 6.988248328953574
Mean Squared Error : 61.91142401337495
r2 score : 0.1808399826649818
Origional Values: [array([283.896], dtype=object), array([277.432], dtype=object), array([273.84], dtype=object)]
Predicted Values: [297.12373468 287.28961669 280.75778987]
Explained Variance Score: 0.6160958592262832
Maximam Error : 13.227734675362626
Mean Absolute Error : 10.001047081274598
Mean Squared Error : 106.66712942673577
r2 score : -5.161420749946237
Origional Values: [array([48.975], dtype=object), array([49.611], dtype=object), array([51.674], dtype=object)]
Predicted Values: [51.87208232 47.46285448 48.06488731]
Explained Variance Score: -4.853406130437532
Maximam Error : 3.6091126888109315
Mean Absolute Error : 2.884780175649292
Mean Squared Error : 8.677769846989651
r2 score : -5.538258442683104
Origional Values: [array([69.888], dtype=object), array([70.183], dtype=object), array([72.506], dtype=object)]
Predicted Values: [79.65256321 69.48938597 69.78208707]
Explained Variance Score: -20.8409296731522
Maximam Error : 9.764563210330408
Mean Absolute Error : 4.394030056571272
Mean Squared Error : 34.415832258551326
r2 score : -24.106226051399936
Origional Values: [array([1324.769], dtype=object), array([1278.977], dtype=object), array([1167.53], dtype=object)]
Predicted Values: [1379.98118624 1372.70714457 1325.27827996]
Explained Variance Score: 0.5898314749653057
Maximam Error : 157.7482799556601
Mean Absolute Error : 102.23020359097791
Mean Squared Error : 12239.415113506599
r2 score : -1.8071019883208561
Origional Values: [array([502.969], dtype=object), array([569.349], dtype=object), array([595.054], dtype=object)]
Predicted Values: [544.68000034 547.33953382 619.48494967]
Explained Variance Score: 0.5190259710705805
Maximam Error : 41.71100033909562
Mean Absolute Error : 29.38380539524195
Mean Squared Error : 940.365150812785
r2 score : 0.3752510057263977
Origional Values: [array([72.008], dtype=object), array([70.825], dtype=object), array([70.497], dtype=object)]
Predicted Values: [77.65559916 76.57085211 75.32738724]
Explained Variance Score: 0.6001352684199652
Maximam Error : 5.745852113128393
Mean Absolute Error : 5.407946168814457
Mean Squared Error : 29.414277865511213
r2 score : -68.84563343976664
Origional Values: [array([30.164], dtype=object), array([29.494], dtype=object), array([30.648], dtype=object)]
Predicted Values: [32.20088608 30.34788724 29.68326774]
Explained Variance Score: -5.807666062753835
Maximam Error : 2.0368860759639986
Mean Absolute Error : 1.2851685242596471
Mean Squared Error : 1.9362455441383357
r2 score : -7.648792527388846
Origional Values: [array([15.195], dtype=object), array([14.321], dtype=object), array([14.0], dtype=object)]
Predicted Values: [15.91619536 15.2488679 14.40358645]
Explained Variance Score: 0.8176598818874089
Maximam Error : 0.9278679025676588
Mean Absolute Error : 0.6842165728220237
Mean Squared Error : 0.5146478735622654
r2 score : -1.0182779617351483
Origional Values: [array([330.458], dtype=object), array([328.481], dtype=object), array([313.451], dtype=object)]
Predicted Values: [330.21689906 334.94361466 332.94303304]
Explained Variance Score: -0.16386679178546837
Maximam Error : 19.492033043456786
Mean Absolute Error : 8.731916215588228
Mean Squared Error : 140.58762337355088
r2 score : -1.437712664046427
Origional Values: [array([277.159], dtype=object), array([279.68], dtype=object), array([293.817], dtype=object)]
Predicted Values: [292.6801947 269.96167209 272.41249992]
Explained Variance Score: -3.4182565767730715
Maximam Error : 21.40450007814951
Mean Absolute Error : 15.548007562436965
Mean Squared Error : 264.50200196770606
r2 score : -3.921484562446972
Origional Values: [array([76.184], dtype=object), array([74.291], dtype=object), array([70.196], dtype=object)]
Predicted Values: [76.90509107 76.03625092 74.16937873]
Explained Variance Score: 0.7048347295233084
Maximam Error : 3.973378728045276
Mean Absolute Error : 2.1465735730478457
Mean Squared Error : 6.451203875073065
r2 score : -0.03295254253818514
Origional Values: [array([98.287], dtype=object), array([90.92], dtype=object), array([94.16], dtype=object)]
Predicted Values: [99.10789701 99.53269039 92.13445678]
Explained Variance Score: -1.2247053938734962
Maximam Error : 8.612690391530805
Mean Absolute Error : 3.8197102097700935
Mean Squared Error : 26.31837768059708
r2 score : -1.895579432937709
Origional Values: [array([211.848], dtype=object), array([214.94], dtype=object), array([210.858], dtype=object)]
Predicted Values: [208.55067901 209.93954097 212.99384078]
Explained Variance Score: -2.0638465835492505
Maximam Error : 5.000459028068548
Mean Absolute Error : 3.477873601032788
Mean Squared Error : 13.479577357011323
r2 score : -3.4596151837639066
Origional Values: [array([255.149], dtype=object), array([239.72], dtype=object), array([235.388], dtype=object)]
Predicted Values: [260.19359503 266.11907322 250.06283805]
Explained Variance Score: -0.06008740807594126
Maximam Error : 26.399073220737904
Mean Absolute Error : 15.37283543343085
Mean Squared Error : 312.5699592552619
r2 score : -3.3458274011156526
Origional Values: [array([26.991], dtype=object), array([28.711], dtype=object), array([29.138], dtype=object)]
Predicted Values: [28.00401903 27.97146791 29.72109073]
Explained Variance Score: 0.35414366358474025
Maximam Error : 1.0130190332958335
Mean Absolute Error : 0.7785472847133962
Mean Squared Error : 0.6377033580094102
r2 score : 0.25947355817453555
Origional Values: [array([368.813], dtype=object), array([369.74], dtype=object), array([374.947], dtype=object)]
Predicted Values: [370.41022861 382.82437138 383.78560679]
Explained Variance Score: -2.0857360018092854
Maximam Error : 13.084371377038508
Mean Absolute Error : 7.840068925800172
Mean Squared Error : 83.9576278429986
r2 score : -10.518904647302708
Origional Values: [array([612.671], dtype=object), array([590.239], dtype=object), array([575.797], dtype=object)]
Predicted Values: [614.70934025 603.65367379 581.57580412]
Explained Variance Score: 0.9026200127376491
Maximam Error : 13.414673793986026
Mean Absolute Error : 7.077272723128924
Mean Squared Error : 72.50096035430211
r2 score : 0.685000281913184
Origional Values: [array([5837.047], dtype=object), array([5854.868], dtype=object), array([5794.513], dtype=object)]
Predicted Values: [5638.13245242 5747.29172273 5764.83726904]
Explained Variance Score: -6.462221324667852
Maximam Error : 198.91454758174677
Mean Absolute Error : 112.05551860574694
Mean Squared Error : 17340.10055992791
r2 score : -26.049504508547106
Origional Values: [array([123.209], dtype=object), array([131.967], dtype=object), array([152.455], dtype=object)]
Predicted Values: [123.27191637 129.32110014 138.48063531]
Explained Variance Score: 0.7538691282819695
Maximam Error : 13.974364689337023
Mean Absolute Error : 5.561060306608293
Mean Squared Error : 67.42920434429276
r2 score : 0.5510669649348865
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [32676.67222409 33314.92505903 33356.1958534 ]
Explained Variance Score: -189.95552443762853
Maximam Error : 1080.1958533956931
Mean Absolute Error : 818.731045502951
Mean Squared Error : 764153.5235053449
r2 score : -1554.0961703723356
seeno=Y.index[-3:]
i=0
while i<len(VVIP1):
plt.plot(seeno,VVIP1[i])
plt.plot(seeno,VVIP2[i])
plt.plot(seeno,VVIP3[i])
plt.plot(seeno,VVIP4[i])
plt.legend(['Predicted LR','Predicted RF','Predicted MLP'
,'Actual CO2'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.title(str(CO2_FINAL_DF_FINAL.columns[i]))
i=i+1
plt.show()
i=0
list_2016=list()
list_2017=list()
list_2018=list()
list_2019=list()
while i<len(CO2_FINAL_DF_FINAL.T):
series=CO2_FINAL_DF_FINAL[CO2_FINAL_DF_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
Model = MLPRegressor()
Model = Model.fit(X, Y)
train_fit = Model.predict(Y[-1:])
list_2016.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2017.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2018.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2019.append(train_fit[0])
i=i+1
CO2_FINAL_DF['F2016']=list_2016
CO2_FINAL_DF['F2017']=list_2017
CO2_FINAL_DF['F2018']=list_2018
CO2_FINAL_DF['F2019']=list_2019
CO2_FINAL_DF.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Argentina | AR | ARG | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 141.18 | 153.44 | 166.89 | ... | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 | 225.07 | 234.45 | 244.17 | 254.25 |
| 7 | 8 | Australia | AU | AUS | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 418.42 | 420.98 | 444.59 | ... | 446.21 | 450.35 | 469.37 | 448.02 | 423.10 | 426.40 | 432.60 | 438.87 | 445.21 | 451.64 |
| 13 | 14 | Austria | AT | AUT | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 95.26 | 95.39 | 94.53 | ... | 89.74 | 90.34 | 86.19 | 86.53 | 82.95 | 83.44 | 82.90 | 82.36 | 81.84 | 81.31 |
| 19 | 20 | Belgium | BE | BEL | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 131.71 | 129.89 | 129.69 | ... | 127.70 | 122.48 | 117.93 | 117.66 | 112.33 | 117.83 | 116.80 | 115.79 | 114.79 | 113.81 |
| 25 | 26 | Brazil | BR | BRA | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 312.82 | 330.87 | 363.68 | ... | 458.99 | 486.29 | 510.59 | 542.57 | 555.96 | 475.38 | 492.11 | 509.40 | 527.29 | 545.78 |
5 rows × 22 columns
CO2_Grass_Exp=Actual_df[Actual_df['Indicator']=='C02 Emissions Embodied in Gross Exports']
CO2_Grass_Exp.isnull().sum()
ObjectId 0 Country 0 ISO2 2 ISO3 0 Indicator 0 Code 0 Unit 0 F2005 0 F2006 0 F2007 0 F2008 0 F2009 0 F2010 0 F2011 0 F2012 0 F2013 0 F2014 0 F2015 0 F2016 9 F2017 9 F2018 9 F2019 9 dtype: int64
CO2_Grass_ExpNOTAV=CO2_Grass_Exp[CO2_Grass_Exp['F2019'].isna()]
CO2_Grass_ExpAV=CO2_Grass_Exp[~CO2_Grass_Exp['F2019'].isna()]
CO2_Grass_ExpNOTAV_FINAL=CO2_Grass_ExpNOTAV[['Country','F2005', 'F2006', 'F2007', 'F2008', 'F2009', 'F2010', 'F2011', 'F2012',
'F2013', 'F2014', 'F2015']]
CO2_Grass_ExpNOTAV_FINAL=CO2_Grass_ExpNOTAV_FINAL.T
CO2_Grass_ExpNOTAV_FINAL.columns=CO2_Grass_ExpNOTAV_FINAL.iloc[0]
CO2_Grass_ExpNOTAV_FINAL=CO2_Grass_ExpNOTAV_FINAL.iloc[1:]
i=0
VVIP1=list()
while i<len(CO2_Grass_ExpNOTAV_FINAL.T):
series=CO2_Grass_ExpNOTAV_FINAL[CO2_Grass_ExpNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = LinearRegression()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP1.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([26.565], dtype=object), array([27.168], dtype=object), array([21.294], dtype=object)]
Predicted Values: [[30.06672165]
[26.53360493]
[27.0718741 ]]
Explained Variance Score: -0.012044537165450286
Maximam Error : 5.777874104082496
Mean Absolute Error : 3.3046636064030785
Mean Squared Error : 15.349446921503024
r2 score : -1.2049956978054444
Origional Values: [array([72.751], dtype=object), array([71.156], dtype=object), array([70.603], dtype=object)]
Predicted Values: [[81.0455358 ]
[81.34331351]
[81.5238363 ]]
Explained Variance Score: -0.4762197552203944
Maximam Error : 10.920836297306764
Mean Absolute Error : 9.800895200711755
Mean Squared Error : 97.28178201126684
r2 score : -116.30530172701606
Origional Values: [array([19.187], dtype=object), array([21.543], dtype=object), array([21.498], dtype=object)]
Predicted Values: [[17.98096526]
[20.63961073]
[23.49326392]]
Explained Variance Score: -0.7204873282894697
Maximam Error : 1.9952639224286024
Mean Absolute Error : 1.3682293106786279
Mean Squared Error : 2.08390336305045
r2 score : -0.7216836882324742
Origional Values: [array([1947.098], dtype=object), array([1882.403], dtype=object), array([1737.099], dtype=object)]
Predicted Values: [[1829.63769809]
[1873.75859455]
[1836.18931179]]
Explained Variance Score: -0.013596324473839738
Maximam Error : 117.46030191141517
Mean Absolute Error : 75.06500638364226
Mean Squared Error : 7896.846053490374
r2 score : -0.02411211271109792
Origional Values: [array([40.624], dtype=object), array([42.747], dtype=object), array([46.679], dtype=object)]
Predicted Values: [[39.10871383]
[39.9478078 ]
[40.86275208]]
Explained Variance Score: 0.48350856223652494
Maximam Error : 5.816247921313888
Mean Absolute Error : 3.376908761405796
Mean Squared Error : 14.65343633295747
r2 score : -1.3287853646760288
Origional Values: [array([91.796], dtype=object), array([76.162], dtype=object), array([76.725], dtype=object)]
Predicted Values: [[78.13698946]
[82.64537551]
[75.67572185]]
Explained Variance Score: -0.3170083163569706
Maximam Error : 13.659010536990891
Mean Absolute Error : 7.0638880633322385
Mean Squared Error : 76.5679038096462
r2 score : -0.4603711667509438
Origional Values: [array([9.793], dtype=object), array([9.159], dtype=object), array([8.715], dtype=object)]
Predicted Values: [[10.59753952]
[10.58618895]
[10.56574504]]
Explained Variance Score: 0.05651788765872312
Maximam Error : 1.850745036728318
Mean Absolute Error : 1.3608245041701281
Mean Squared Error : 2.0364697814481816
r2 score : -9.406812285105895
Origional Values: [array([101.144], dtype=object), array([115.035], dtype=object), array([131.345], dtype=object)]
Predicted Values: [[ 91.33749691]
[102.26100912]
[114.2675377 ]]
Explained Variance Score: 0.9415111376953299
Maximam Error : 17.077462295924732
Mean Absolute Error : 13.219318755531583
Mean Squared Error : 183.66068810656944
r2 score : -0.20558286515588575
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [[0.]
[0.]
[0.]]
Explained Variance Score: 1.0
Maximam Error : 0.0
Mean Absolute Error : 0.0
Mean Squared Error : 0.0
r2 score : 1.0
i=0
VVIP2=list()
while i<len(CO2_Grass_ExpNOTAV_FINAL.T):
series=CO2_Grass_ExpNOTAV_FINAL[CO2_Grass_ExpNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = RandomForestRegressor()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP2.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([26.565], dtype=object), array([27.168], dtype=object), array([21.294], dtype=object)]
Predicted Values: [33.70318 33.70318 33.70318]
Explained Variance Score: 0.0
Maximam Error : 12.409180000000067
Mean Absolute Error : 8.694180000000067
Mean Squared Error : 82.54997987240117
r2 score : -10.858560859126179
Origional Values: [array([72.751], dtype=object), array([71.156], dtype=object), array([70.603], dtype=object)]
Predicted Values: [80.44044 78.36798 78.36798]
Explained Variance Score: 0.9277194157672654
Maximam Error : 7.764979999999909
Mean Absolute Error : 7.5554666666665895
Mean Squared Error : 57.145019144798844
r2 score : -67.90718461757177
Origional Values: [array([19.187], dtype=object), array([21.543], dtype=object), array([21.498], dtype=object)]
Predicted Values: [16.63211 16.63211 16.63211]
Explained Variance Score: 0.0
Maximam Error : 4.910890000000023
Mean Absolute Error : 4.1105566666666915
Mean Squared Error : 18.107062998766867
r2 score : -13.959731607295259
Origional Values: [array([1947.098], dtype=object), array([1882.403], dtype=object), array([1737.099], dtype=object)]
Predicted Values: [1753.31751 1753.31751 1753.31751]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 193.78048999999692
Mean Absolute Error : 113.02816333333233
Mean Squared Error : 18158.994033266146
r2 score : -1.354971290835369
Origional Values: [array([40.624], dtype=object), array([42.747], dtype=object), array([46.679], dtype=object)]
Predicted Values: [38.84393 39.04881 40.38676]
Explained Variance Score: 0.45669194504442856
Maximam Error : 6.292240000000014
Mean Absolute Error : 3.9234999999999993
Mean Squared Error : 18.812514232866747
r2 score : -1.9897634126764197
Origional Values: [array([91.796], dtype=object), array([76.162], dtype=object), array([76.725], dtype=object)]
Predicted Values: [78.9544 78.9544 73.61162]
Explained Variance Score: 0.20754590267690243
Maximam Error : 12.84160000000007
Mean Absolute Error : 6.249126666666679
Mean Squared Error : 60.79910778146721
r2 score : -0.15961466293990134
Origional Values: [array([9.793], dtype=object), array([9.159], dtype=object), array([8.715], dtype=object)]
Predicted Values: [10.87534 10.9549 10.75074]
Explained Variance Score: 0.16211516668408077
Maximam Error : 2.0357399999999757
Mean Absolute Error : 1.6379933333333099
Mean Squared Error : 2.8469846777332557
r2 score : -13.548723182464066
Origional Values: [array([101.144], dtype=object), array([115.035], dtype=object), array([131.345], dtype=object)]
Predicted Values: [86.79874 86.79874 86.79874]
Explained Variance Score: -2.220446049250313e-16
Maximam Error : 44.54625999999989
Mean Absolute Error : 29.042593333333226
Mean Squared Error : 995.8140477475936
r2 score : -5.536708346368661
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [0. 0. 0.]
Explained Variance Score: 1.0
Maximam Error : 0.0
Mean Absolute Error : 0.0
Mean Squared Error : 0.0
r2 score : 1.0
i=0
VVIP3=list()
VVIP4=list()
while i<len(CO2_Grass_ExpNOTAV_FINAL.T):
series=CO2_Grass_ExpNOTAV_FINAL[CO2_Grass_ExpNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = MLPRegressor ()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP3.append(pre)
VVIP4.append(y_test)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([26.565], dtype=object), array([27.168], dtype=object), array([21.294], dtype=object)]
Predicted Values: [29.96770163 26.14220591 26.72503345]
Explained Variance Score: -0.04413981202872508
Maximam Error : 5.431033448281557
Mean Absolute Error : 3.286509722707038
Mean Squared Error : 14.0422520774339
r2 score : -1.0172131006795517
Origional Values: [array([72.751], dtype=object), array([71.156], dtype=object), array([70.603], dtype=object)]
Predicted Values: [75.11473119 72.51786975 70.94356568]
Explained Variance Score: 0.1773576454242024
Maximam Error : 2.363731186496608
Mean Absolute Error : 1.3553888713275721
Mean Squared Error : 2.5192997705471734
r2 score : -2.037847514867805
Origional Values: [array([19.187], dtype=object), array([21.543], dtype=object), array([21.498], dtype=object)]
Predicted Values: [17.67483812 19.9446891 22.37901335]
Explained Variance Score: -0.09072177863859854
Maximam Error : 1.5983108993804507
Mean Absolute Error : 1.3304953761065452
Mean Squared Error : 1.8724719341138298
r2 score : -0.5470028230665351
Origional Values: [array([1947.098], dtype=object), array([1882.403], dtype=object), array([1737.099], dtype=object)]
Predicted Values: [1973.99872974 2054.12371864 1985.89667418]
Explained Variance Score: -0.09731747260193768
Maximam Error : 248.7976741842965
Mean Absolute Error : 149.1397075221906
Mean Squared Error : 30703.97904990995
r2 score : -2.981882974601273
Origional Values: [array([40.624], dtype=object), array([42.747], dtype=object), array([46.679], dtype=object)]
Predicted Values: [38.83309786 40.73089465 42.80024368]
Explained Variance Score: 0.860865054530912
Maximam Error : 3.878756319707037
Mean Absolute Error : 2.5619212693116205
Mean Squared Error : 7.438920613362601
r2 score : -0.18222436428938704
Origional Values: [array([91.796], dtype=object), array([76.162], dtype=object), array([76.725], dtype=object)]
Predicted Values: [83.18858754 93.43132963 77.59675732]
Explained Variance Score: -1.1792700849087265
Maximam Error : 17.269329626444275
Mean Absolute Error : 8.916166468104516
Mean Squared Error : 124.35908525352482
r2 score : -1.371887088345371
Origional Values: [array([9.793], dtype=object), array([9.159], dtype=object), array([8.715], dtype=object)]
Predicted Values: [10.24334701 9.9171779 9.32970284]
Explained Variance Score: 0.9191689070672328
Maximam Error : 0.7581778965211985
Mean Absolute Error : 0.6077425827893533
Mean Squared Error : 0.3851685781495806
r2 score : -0.9682968671763807
Origional Values: [array([101.144], dtype=object), array([115.035], dtype=object), array([131.345], dtype=object)]
Predicted Values: [ 95.75858916 109.30113146 124.18635501]
Explained Variance Score: 0.9961375090869726
Maximam Error : 7.158644994211556
Mean Absolute Error : 6.0926414588294096
Mean Squared Error : 37.708698842129344
r2 score : 0.7524730977539629
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [-0.04506452 -0.04506452 -0.04506452]
Explained Variance Score: 1.0
Maximam Error : 0.04506451745651352
Mean Absolute Error : 0.04506451745651352
Mean Squared Error : 0.002030810733588412
r2 score : 0.0
seeno=Y.index[-3:]
i=0
while i<len(VVIP1):
plt.plot(seeno,VVIP1[i])
plt.plot(seeno,VVIP2[i])
plt.plot(seeno,VVIP3[i])
plt.plot(seeno,VVIP4[i])
plt.legend(['Predicted LR','Predicted RF','Predicted MLP'
,'Actual CO2'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.title(str(CO2_Grass_ExpNOTAV_FINAL.columns[i]))
i=i+1
plt.show()
i=0
list_2016=list()
list_2017=list()
list_2018=list()
list_2019=list()
while i<len(CO2_Grass_ExpNOTAV_FINAL.T):
series=CO2_Grass_ExpNOTAV_FINAL[CO2_Grass_ExpNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
Model = MLPRegressor()
Model = Model.fit(X, Y)
train_fit = Model.predict(Y[-1:])
list_2016.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2017.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2018.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2019.append(train_fit[0])
i=i+1
CO2_Grass_ExpNOTAV['F2016']=list_2016
CO2_Grass_ExpNOTAV['F2017']=list_2017
CO2_Grass_ExpNOTAV['F2018']=list_2018
CO2_Grass_ExpNOTAV['F2019']=list_2019
CO2_Grass_Exp=pd.concat([CO2_Grass_ExpNOTAV,CO2_Grass_ExpAV])
CO2_Grass_IMP=Actual_df[Actual_df['Indicator']=='C02 Emissions Embodied in Gross Imports']
CO2_Grass_IMP.isnull().sum()
ObjectId 0 Country 0 ISO2 2 ISO3 0 Indicator 0 Code 0 Unit 0 F2005 0 F2006 0 F2007 0 F2008 0 F2009 0 F2010 0 F2011 0 F2012 0 F2013 0 F2014 0 F2015 0 F2016 9 F2017 9 F2018 9 F2019 9 dtype: int64
CO2_Grass_IMPNOTAV=CO2_Grass_IMP[CO2_Grass_IMP['F2019'].isna()]
CO2_Grass_IMPAV=CO2_Grass_IMP[~CO2_Grass_IMP['F2019'].isna()]
CO2_Grass_IMPNOTAV_FINAL=CO2_Grass_IMPNOTAV[['Country','F2005', 'F2006', 'F2007', 'F2008', 'F2009', 'F2010', 'F2011', 'F2012',
'F2013', 'F2014', 'F2015']]
CO2_Grass_IMPNOTAV_FINAL=CO2_Grass_IMPNOTAV_FINAL.T
CO2_Grass_IMPNOTAV_FINAL.columns=CO2_Grass_IMPNOTAV_FINAL.iloc[0]
CO2_Grass_IMPNOTAV_FINAL=CO2_Grass_IMPNOTAV_FINAL.iloc[1:]
CO2_Grass_IMPNOTAV_FINAL
| Country | Argentina | Belgium | Colombia | G20 | Ireland | Saudi Arabia | Slovenia, Rep. of | Vietnam | World |
|---|---|---|---|---|---|---|---|---|---|
| F2005 | 28.14 | 87.83 | 21.1 | 1484.69 | 41.17 | 55.03 | 11.79 | 46.01 | 0.0 |
| F2006 | 32.24 | 92.67 | 23.54 | 1600.61 | 45.02 | 70.23 | 13.27 | 53.52 | 0.0 |
| F2007 | 37.97 | 99.73 | 27.97 | 1629.4 | 48.96 | 87.93 | 14.91 | 67.43 | 0.0 |
| F2008 | 41.69 | 113.26 | 31.17 | 1719.69 | 45.76 | 104.24 | 15.52 | 75.26 | 0.0 |
| F2009 | 30.43 | 90.02 | 24.33 | 1383.64 | 39.46 | 90.07 | 10.65 | 68.95 | 0.0 |
| F2010 | 40.14 | 89.81 | 29.22 | 1558.79 | 38.46 | 94.8 | 11.94 | 74.46 | 0.0 |
| F2011 | 47.78 | 99.17 | 36.18 | 1708.0 | 34.98 | 102.58 | 12.45 | 78.4 | 0.0 |
| F2012 | 44.06 | 90.75 | 39.56 | 1667.58 | 34.71 | 112.94 | 11.03 | 77.21 | 0.0 |
| F2013 | 47.05 | 88.39 | 39.71 | 1643.66 | 36.59 | 118.3 | 10.59 | 90.8 | 0.0 |
| F2014 | 41.76 | 87.94 | 42.57 | 1605.44 | 39.88 | 130.3 | 10.68 | 99.28 | 0.0 |
| F2015 | 42.24 | 86.55 | 36.09 | 1469.53 | 40.46 | 131.75 | 9.82 | 110.56 | 0.0 |
i=0
VVIP1=list()
while i<len(CO2_Grass_IMPNOTAV_FINAL.T):
series=CO2_Grass_IMPNOTAV_FINAL[CO2_Grass_IMPNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = LinearRegression()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP1.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([47.053], dtype=object), array([41.757], dtype=object), array([42.242], dtype=object)]
Predicted Values: [[41.67489363]
[42.7136682 ]
[40.87252022]]
Explained Variance Score: -0.19796117542287583
Maximam Error : 5.3781063748623055
Mean Absolute Error : 2.5680847851954547
Mean Squared Error : 10.571572363747398
r2 score : -0.8500255028271373
Origional Values: [array([88.389], dtype=object), array([87.941], dtype=object), array([86.547], dtype=object)]
Predicted Values: [[96.75316741]
[96.87107764]
[96.89347008]]
Explained Variance Score: -0.12986351987760503
Maximam Error : 10.346470084693337
Mean Absolute Error : 9.213571710914016
Mean Squared Error : 85.58500876578438
r2 score : -138.11476140674623
Origional Values: [array([39.714], dtype=object), array([42.571], dtype=object), array([36.086], dtype=object)]
Predicted Values: [[39.67676232]
[39.79739064]
[42.04990769]]
Explained Variance Score: -0.8909051802002568
Maximam Error : 5.963907693104048
Mean Absolute Error : 2.924918243839123
Mean Squared Error : 14.420830165083494
r2 score : -1.04776511068752
Origional Values: [array([1643.656], dtype=object), array([1605.442], dtype=object), array([1469.532], dtype=object)]
Predicted Values: [[1600.42234761]
[1603.05588092]
[1607.26226928]]
Explained Variance Score: -0.07558175049371241
Maximam Error : 137.73026928490413
Mean Absolute Error : 61.11668025287986
Mean Squared Error : 6948.156446838652
r2 score : -0.24442103562038842
Origional Values: [array([36.589], dtype=object), array([39.88], dtype=object), array([40.462], dtype=object)]
Predicted Values: [[34.85641202]
[36.45756173]
[39.2649052 ]]
Explained Variance Score: 0.6906899258749328
Maximam Error : 3.422438267619789
Mean Absolute Error : 2.1173736809890187
Mean Squared Error : 5.382660249540375
r2 score : -0.8511580009740813
Origional Values: [array([118.302], dtype=object), array([130.301], dtype=object), array([131.752], dtype=object)]
Predicted Values: [[110.92484801]
[114.21124004]
[121.55862553]]
Explained Variance Score: 0.6372665202068613
Maximam Error : 16.089759964757093
Mean Absolute Error : 11.220095476157937
Mean Squared Error : 139.06921011729963
r2 score : -2.827781378227293
Origional Values: [array([10.588], dtype=object), array([10.684], dtype=object), array([9.82], dtype=object)]
Predicted Values: [[12.49644351]
[12.42053745]
[12.43698662]]
Explained Variance Score: 0.02880653729103655
Maximam Error : 2.6169866152368044
Mean Absolute Error : 2.087322523792741
Mean Squared Error : 4.502112625781339
r2 score : -29.113660007634245
Origional Values: [array([90.799], dtype=object), array([99.284], dtype=object), array([110.562], dtype=object)]
Predicted Values: [[77.36814969]
[85.60710033]
[90.75189694]]
Explained Variance Score: 0.867113795050057
Maximam Error : 19.810103062888786
Mean Absolute Error : 15.639284346402746
Mean Squared Error : 253.29516929796523
r2 score : -2.8653663210425253
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [[0.]
[0.]
[0.]]
Explained Variance Score: 1.0
Maximam Error : 0.0
Mean Absolute Error : 0.0
Mean Squared Error : 0.0
r2 score : 1.0
i=0
VVIP2=list()
while i<len(CO2_Grass_IMPNOTAV_FINAL.T):
series=CO2_Grass_IMPNOTAV_FINAL[CO2_Grass_IMPNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = RandomForestRegressor()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP2.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([47.053], dtype=object), array([41.757], dtype=object), array([42.242], dtype=object)]
Predicted Values: [34.01317 40.15027 34.51293]
Explained Variance Score: -2.818947814702663
Maximam Error : 13.039829999999974
Mean Absolute Error : 7.458543333333303
Mean Squared Error : 77.45242359556626
r2 score : -12.554176614156477
Origional Values: [array([88.389], dtype=object), array([87.941], dtype=object), array([86.547], dtype=object)]
Predicted Values: [92.81341 94.56478 94.56478]
Explained Variance Score: -2.5566396588780576
Maximam Error : 8.017780000000073
Mean Absolute Error : 6.355323333333388
Mean Squared Error : 42.57822048830076
r2 score : -68.20907142235201
Origional Values: [array([39.714], dtype=object), array([42.571], dtype=object), array([36.086], dtype=object)]
Predicted Values: [36.1785 36.1785 36.1785]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 6.39249999999997
Mean Absolute Error : 3.3401666666666565
Mean Squared Error : 17.79079091666647
r2 score : -1.5263012263257667
Origional Values: [array([1643.656], dtype=object), array([1605.442], dtype=object), array([1469.532], dtype=object)]
Predicted Values: [1665.44546 1677.08846 1651.01477]
Explained Variance Score: 0.2029674135794991
Maximam Error : 181.48276999999985
Mean Absolute Error : 91.63956333333287
Mean Squared Error : 12847.99720149864
r2 score : -1.3010877929225857
Origional Values: [array([36.589], dtype=object), array([39.88], dtype=object), array([40.462], dtype=object)]
Predicted Values: [35.21609 35.21609 39.88045]
Explained Variance Score: -0.07463108892959158
Maximam Error : 4.663910000000001
Mean Absolute Error : 2.2061233333333328
Mean Squared Error : 7.991712919566669
r2 score : -1.7484408501924356
Origional Values: [array([118.302], dtype=object), array([130.301], dtype=object), array([131.752], dtype=object)]
Predicted Values: [96.21319 96.21319 96.21319]
Explained Variance Score: -4.440892098500626e-16
Maximam Error : 35.538810000000026
Mean Absolute Error : 30.571810000000017
Mean Squared Error : 970.9671113427677
r2 score : -25.725181113304224
Origional Values: [array([10.588], dtype=object), array([10.684], dtype=object), array([9.82], dtype=object)]
Predicted Values: [12.42053 12.42053 12.42053]
Explained Variance Score: 0.0
Maximam Error : 2.600529999999994
Mean Absolute Error : 2.0565299999999946
Mean Squared Error : 4.378819640899977
r2 score : -28.288979832646536
Origional Values: [array([90.799], dtype=object), array([99.284], dtype=object), array([110.562], dtype=object)]
Predicted Values: [74.91723 74.91723 74.91723]
Explained Variance Score: -2.220446049250313e-16
Maximam Error : 35.64476999999992
Mean Absolute Error : 25.29776999999993
Mean Squared Error : 705.506575639563
r2 score : -9.76625884461001
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [0. 0. 0.]
Explained Variance Score: 1.0
Maximam Error : 0.0
Mean Absolute Error : 0.0
Mean Squared Error : 0.0
r2 score : 1.0
i=0
VVIP3=list()
VVIP4=list()
while i<len(CO2_Grass_IMPNOTAV_FINAL.T):
series=CO2_Grass_IMPNOTAV_FINAL[CO2_Grass_IMPNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = MLPRegressor ()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP3.append(pre)
VVIP4.append(y_test)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([47.053], dtype=object), array([41.757], dtype=object), array([42.242], dtype=object)]
Predicted Values: [45.90472983 48.96925918 43.53761679]
Explained Variance Score: -1.1559555117022091
Maximam Error : 7.212259179955403
Mean Absolute Error : 3.218715381862019
Mean Squared Error : 18.33794324746315
r2 score : -2.209140656648506
Origional Values: [array([88.389], dtype=object), array([87.941], dtype=object), array([86.547], dtype=object)]
Predicted Values: [90.94594334 88.61334169 88.17035503]
Explained Variance Score: 0.03777690621051699
Maximam Error : 2.556943343644818
Mean Absolute Error : 1.6175466875354658
Mean Squared Error : 3.208428052678665
r2 score : -4.215162205107396
Origional Values: [array([39.714], dtype=object), array([42.571], dtype=object), array([36.086], dtype=object)]
Predicted Values: [42.8766813 43.03917671 46.07348658]
Explained Variance Score: -1.2791961204419726
Maximam Error : 9.987486576831685
Mean Absolute Error : 4.53944819606285
Mean Squared Error : 36.65721018107017
r2 score : -4.205342217099762
Origional Values: [array([1643.656], dtype=object), array([1605.442], dtype=object), array([1469.532], dtype=object)]
Predicted Values: [1688.4832267 1664.26976663 1625.59502626]
Explained Variance Score: 0.5617179433834214
Maximam Error : 156.0630262618747
Mean Absolute Error : 86.5726731978338
Mean Squared Error : 9941.95151547034
r2 score : -0.7806124107349981
Origional Values: [array([36.589], dtype=object), array([39.88], dtype=object), array([40.462], dtype=object)]
Predicted Values: [34.22087261 36.05549406 39.27219047]
Explained Variance Score: 0.6006387939210931
Maximam Error : 3.8245059448453134
Mean Absolute Error : 2.4608142879907198
Mean Squared Error : 7.216839922286272
r2 score : -1.4819532247145206
Origional Values: [array([118.302], dtype=object), array([130.301], dtype=object), array([131.752], dtype=object)]
Predicted Values: [122.63024308 128.42547585 141.38187568]
Explained Variance Score: 0.3915044122816692
Maximam Error : 9.629875684077973
Mean Absolute Error : 5.277880973380931
Mean Squared Error : 38.328594905760944
r2 score : -0.05496739148904317
Origional Values: [array([10.588], dtype=object), array([10.684], dtype=object), array([9.82], dtype=object)]
Predicted Values: [11.00505095 10.57298479 10.66661538]
Explained Variance Score: -0.025937483281288864
Maximam Error : 0.8466153777181216
Mean Absolute Error : 0.45822717999307905
Mean Squared Error : 0.30100448991309287
r2 score : -1.0133540902791474
Origional Values: [array([90.799], dtype=object), array([99.284], dtype=object), array([110.562], dtype=object)]
Predicted Values: [ 81.72150024 95.96734313 104.86313183]
Explained Variance Score: 0.9147500779665544
Maximam Error : 9.077499755957405
Mean Absolute Error : 6.031008265223136
Mean Squared Error : 41.959437674920906
r2 score : 0.35968539120566223
Origional Values: [array([0.0], dtype=object), array([0.0], dtype=object), array([0.0], dtype=object)]
Predicted Values: [0.00012388 0.00012388 0.00012388]
Explained Variance Score: 1.0
Maximam Error : 0.00012387792769983155
Mean Absolute Error : 0.00012387792769983155
Mean Squared Error : 1.5345740971204692e-08
r2 score : 0.0
seeno=Y.index[-3:]
i=0
while i<len(VVIP1):
plt.plot(seeno,VVIP1[i])
plt.plot(seeno,VVIP2[i])
plt.plot(seeno,VVIP3[i])
plt.plot(seeno,VVIP4[i])
plt.legend(['Predicted LR','Predicted RF','Predicted MLP'
,'Actual CO2'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.title(str(CO2_Grass_IMPNOTAV_FINAL.columns[i]))
i=i+1
plt.show()
i=0
list_2016=list()
list_2017=list()
list_2018=list()
list_2019=list()
while i<len(CO2_Grass_IMPNOTAV_FINAL.T):
series=CO2_Grass_IMPNOTAV_FINAL[CO2_Grass_IMPNOTAV_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
Model = MLPRegressor()
Model = Model.fit(X, Y)
train_fit = Model.predict(Y[-1:])
list_2016.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2017.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2018.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2019.append(train_fit[0])
i=i+1
CO2_Grass_IMPNOTAV['F2016']=list_2016
CO2_Grass_IMPNOTAV['F2017']=list_2017
CO2_Grass_IMPNOTAV['F2018']=list_2018
CO2_Grass_IMPNOTAV['F2019']=list_2019
CO2_Grass_IMP=pd.concat([CO2_Grass_IMPNOTAV,CO2_Grass_IMPAV])
CO2_PROD=Actual_df[Actual_df['Indicator']=='C02 Emissions Embodied in Production']
CO2_PROD.isnull().sum()
ObjectId 0 Country 0 ISO2 2 ISO3 0 Indicator 0 Code 0 Unit 0 F2005 0 F2006 0 F2007 0 F2008 0 F2009 0 F2010 0 F2011 0 F2012 0 F2013 0 F2014 0 F2015 0 F2016 66 F2017 66 F2018 66 F2019 66 dtype: int64
CO2_PROD_FINAL=CO2_PROD[['Country','F2005', 'F2006', 'F2007', 'F2008', 'F2009', 'F2010', 'F2011', 'F2012',
'F2013', 'F2014', 'F2015']]
CO2_PROD_FINAL=CO2_PROD_FINAL.T
CO2_PROD_FINAL.columns=CO2_PROD_FINAL.iloc[0]
CO2_PROD_FINAL=CO2_PROD_FINAL.iloc[1:]
CO2_PROD_FINAL
| Country | Argentina | Australia | Austria | Belgium | Brazil | Brunei Darussalam | Bulgaria | Cambodia | Canada | Chile | ... | Sweden | Switzerland | Taiwan Province of China | Thailand | Tunisia | Turkey | United Kingdom | United States | Vietnam | World |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F2005 | 152.27 | 382.1 | 75.27 | 118.87 | 321.14 | 5.16 | 47.1 | 3.04 | 555.35 | 61.25 | ... | 57.77 | 53.69 | 269.41 | 213.52 | 21.45 | 227.13 | 569.09 | 5833.58 | 82.13 | 27069.6 |
| F2006 | 161.49 | 387.6 | 73.21 | 118.0 | 324.48 | 7.58 | 48.08 | 3.39 | 547.01 | 63.67 | ... | 56.6 | 53.27 | 278.82 | 214.84 | 22.57 | 252.59 | 571.22 | 5723.61 | 84.46 | 27924.3 |
| F2007 | 168.48 | 400.35 | 71.77 | 114.79 | 340.54 | 7.59 | 50.9 | 3.99 | 577.37 | 71.94 | ... | 53.85 | 51.56 | 284.18 | 223.18 | 22.95 | 278.48 | 561.28 | 5822.52 | 93.13 | 28979.7 |
| F2008 | 179.92 | 401.04 | 71.3 | 117.72 | 360.19 | 7.76 | 48.48 | 4.07 | 556.61 | 76.4 | ... | 51.82 | 53.98 | 269.51 | 226.49 | 23.2 | 274.43 | 547.51 | 5630.65 | 104.09 | 29202.9 |
| F2009 | 172.69 | 408.03 | 64.08 | 108.39 | 339.52 | 7.97 | 42.9 | 4.87 | 527.58 | 71.89 | ... | 48.9 | 52.39 | 256.65 | 221.35 | 23.25 | 273.38 | 496.72 | 5216.52 | 114.7 | 28815.2 |
| F2010 | 177.26 | 400.08 | 69.74 | 114.63 | 381.49 | 7.3 | 44.4 | 5.19 | 543.28 | 78.31 | ... | 55.66 | 52.78 | 272.17 | 237.85 | 25.48 | 279.04 | 512.23 | 5463.63 | 129.01 | 30489.9 |
| F2011 | 185.18 | 398.54 | 68.31 | 103.33 | 400.36 | 7.62 | 49.56 | 5.5 | 550.91 | 85.05 | ... | 49.8 | 49.84 | 271.6 | 235.28 | 24.54 | 298.52 | 476.07 | 5248.63 | 128.95 | 31338.3 |
| F2012 | 189.64 | 397.91 | 65.61 | 102.29 | 431.96 | 7.61 | 44.58 | 5.72 | 546.84 | 85.3 | ... | 45.73 | 49.87 | 263.36 | 252.4 | 25.65 | 309.64 | 496.99 | 5012.73 | 128.72 | 31669.2 |
| F2013 | 184.82 | 395.4 | 66.58 | 101.64 | 460.64 | 7.43 | 39.5 | 6.0 | 555.89 | 89.44 | ... | 45.0 | 50.45 | 264.26 | 262.58 | 25.55 | 297.81 | 482.46 | 5146.64 | 133.6 | 32287.8 |
| F2014 | 188.74 | 385.99 | 62.77 | 95.11 | 484.94 | 7.28 | 41.43 | 6.97 | 557.95 | 82.26 | ... | 45.37 | 46.05 | 266.81 | 259.15 | 26.99 | 321.67 | 444.99 | 5155.53 | 147.78 | 32327.8 |
| F2015 | 195.03 | 392.49 | 64.26 | 101.3 | 461.15 | 6.71 | 43.56 | 8.82 | 556.44 | 86.7 | ... | 43.8 | 44.87 | 268.22 | 267.68 | 27.46 | 336.88 | 430.79 | 5020.0 | 173.29 | 32276.0 |
11 rows × 66 columns
i=0
VVIP1=list()
while i<len(CO2_PROD_FINAL.T):
series=CO2_PROD_FINAL[CO2_PROD_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = LinearRegression()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP1.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([184.817], dtype=object), array([188.737], dtype=object), array([195.027], dtype=object)]
Predicted Values: [[189.82196526]
[186.3380373 ]
[189.17202725]]
Explained Variance Score: -0.16036357598370654
Maximam Error : 5.854972745617658
Mean Absolute Error : 4.419633568062362
Mean Squared Error : 21.69513503979154
r2 score : -0.22667947874927674
Origional Values: [array([395.402], dtype=object), array([385.987], dtype=object), array([392.488], dtype=object)]
Predicted Values: [[399.56414699]
[398.44836329]
[394.26306309]]
Explained Variance Score: -0.3542090915215812
Maximam Error : 12.461363293776287
Mean Absolute Error : 6.132857791597473
Mean Squared Error : 58.58663056304625
r2 score : -2.782585743486814
Origional Values: [array([66.578], dtype=object), array([62.77], dtype=object), array([64.256], dtype=object)]
Predicted Values: [[67.29218576]
[67.65774089]
[66.22265435]]
Explained Variance Score: -0.2452063402322029
Maximam Error : 4.887740888452534
Mean Absolute Error : 2.5228603336629547
Mean Squared Error : 9.422600546797602
r2 score : -2.8371289636755406
Origional Values: [array([101.642], dtype=object), array([95.113], dtype=object), array([101.301], dtype=object)]
Predicted Values: [[104.83909936]
[104.46911935]
[100.75852103]]
Explained Variance Score: -0.8498100797703569
Maximam Error : 9.356119346831207
Mean Absolute Error : 4.365232557515711
Mean Squared Error : 32.684232324923435
r2 score : -2.629991970729347
Origional Values: [array([460.643], dtype=object), array([484.935], dtype=object), array([461.154], dtype=object)]
Predicted Values: [[456.26572719]
[488.01077761]
[514.89513003]]
Explained Variance Score: -4.191005477367883
Maximam Error : 53.741130029898045
Mean Absolute Error : 20.398060150371805
Mean Squared Error : 972.2433273540706
r2 score : -6.570034143977071
Origional Values: [array([7.433], dtype=object), array([7.282], dtype=object), array([6.71], dtype=object)]
Predicted Values: [[7.63842522]
[7.636323 ]
[7.63454962]]
Explained Variance Score: 0.009447708628705032
Maximam Error : 0.92454961784399
Mean Absolute Error : 0.4947659439795012
Mean Squared Error : 0.3408454340343501
r2 score : -2.515021996105427
Origional Values: [array([39.502], dtype=object), array([41.429], dtype=object), array([43.564], dtype=object)]
Predicted Values: [[46.87900442]
[46.68170867]
[46.75656374]]
Explained Variance Score: -0.06035086449543847
Maximam Error : 7.3770044166506
Mean Absolute Error : 5.274092278243702
Mean Squared Error : 30.734535279881015
r2 score : -10.166540439862517
Origional Values: [array([5.997], dtype=object), array([6.969], dtype=object), array([8.821], dtype=object)]
Predicted Values: [[5.98381833]
[6.23217176]
[7.11967005]]
Explained Variance Score: 0.6515069155419373
Maximam Error : 1.7013299496056087
Mean Absolute Error : 0.8171132886644775
Mean Squared Error : 1.1458710708877107
r2 score : 0.16492953670728216
Origional Values: [array([555.889], dtype=object), array([557.952], dtype=object), array([556.444], dtype=object)]
Predicted Values: [[549.69893461]
[550.20749659]
[550.32340063]]
Explained Variance Score: 0.26029182265858897
Maximam Error : 7.744503410490665
Mean Absolute Error : 6.6850560568097235
Mean Squared Error : 45.25199308488113
r2 score : -58.559006045858276
Origional Values: [array([89.437], dtype=object), array([82.263], dtype=object), array([86.705], dtype=object)]
Predicted Values: [[86.10818943]
[89.38637849]
[83.70164842]]
Explained Variance Score: -1.693883775891495
Maximam Error : 7.123378489901313
Mean Absolute Error : 4.4851802141797394
Mean Squared Error : 23.614540549980546
r2 score : -1.7018422254361476
Origional Values: [array([71.888], dtype=object), array([72.416], dtype=object), array([70.399], dtype=object)]
Predicted Values: [[69.8378918 ]
[71.04518595]
[71.34474013]]
Explained Variance Score: -1.2551087136579993
Maximam Error : 2.05010820245937
Mean Absolute Error : 1.455554129847125
Mean Squared Error : 2.325499737400513
r2 score : -2.1884337416908544
Origional Values: [array([9319.037], dtype=object), array([9264.018], dtype=object), array([9280.847], dtype=object)]
Predicted Values: [[9400.93603009]
[9779.06677877]
[9724.83490896]]
Explained Variance Score: -66.89505757202821
Maximam Error : 515.048778766668
Mean Absolute Error : 346.9785726059278
Mean Squared Error : 156369.31964828356
r2 score : -294.11182620171354
Origional Values: [array([79.214], dtype=object), array([82.854], dtype=object), array([82.755], dtype=object)]
Predicted Values: [[72.04220267]
[82.22183118]
[86.0820091 ]]
Explained Variance Score: -5.537957639437621
Maximam Error : 7.171797331085017
Mean Absolute Error : 3.7103250816236275
Mean Squared Error : 20.967767968707175
r2 score : -6.314881459728854
Origional Values: [array([8.296], dtype=object), array([8.43], dtype=object), array([8.003], dtype=object)]
Predicted Values: [[7.60909428]
[7.87586025]
[7.93835437]]
Explained Variance Score: -1.252227721849
Maximam Error : 0.686905719954666
Mean Absolute Error : 0.4352303653831635
Mean Squared Error : 0.26102979616973315
r2 score : -7.210377534748069
Origional Values: [array([15.792], dtype=object), array([15.052], dtype=object), array([15.269], dtype=object)]
Predicted Values: [[15.49246523]
[14.94166227]
[14.11153155]]
Explained Variance Score: -1.151911705285523
Maximam Error : 1.15746845444278
Mean Absolute Error : 0.5224469860777082
Mean Squared Error : 0.4805429062549685
r2 score : -3.9813366646334387
Origional Values: [array([7.534], dtype=object), array([7.324], dtype=object), array([7.325], dtype=object)]
Predicted Values: [[9.09682601]
[8.7463843 ]
[8.68727365]]
Explained Variance Score: 0.27595167482364813
Maximam Error : 1.5628260077948477
Mean Absolute Error : 1.4491613207543523
Mean Squared Error : 2.1071305791350134
r2 score : -215.0371740472438
Origional Values: [array([101.372], dtype=object), array([98.856], dtype=object), array([99.275], dtype=object)]
Predicted Values: [[105.5238336 ]
[101.51245767]
[ 99.33779233]]
Explained Variance Score: -1.355587923964484
Maximam Error : 4.151833595635523
Mean Absolute Error : 2.2903611990321053
Mean Squared Error : 8.099477477349074
r2 score : -5.685663242800028
Origional Values: [array([74.767], dtype=object), array([66.902], dtype=object), array([64.912], dtype=object)]
Predicted Values: [[85.14786465]
[83.85391525]
[80.41693047]]
Explained Variance Score: 0.5610113724607776
Maximam Error : 16.9519152547727
Mean Absolute Error : 14.279236788970659
Mean Squared Error : 211.8442167897856
r2 score : -10.701273824328341
Origional Values: [array([19.019], dtype=object), array([17.642], dtype=object), array([15.602], dtype=object)]
Predicted Values: [[17.65908268]
[16.91031221]
[17.35206754]]
Explained Variance Score: 0.0850266609292305
Maximam Error : 1.7500675386616429
Mean Absolute Error : 1.2805575495801556
Mean Squared Error : 1.8158261764394474
r2 score : 0.07844887670665701
Origional Values: [array([52.903], dtype=object), array([48.379], dtype=object), array([45.243], dtype=object)]
Predicted Values: [[59.74034455]
[59.952366 ]
[59.16807732]]
Explained Variance Score: 0.12115839489635294
Maximam Error : 13.925077322369624
Mean Absolute Error : 10.778595956573726
Mean Squared Error : 124.86661982142978
r2 score : -11.630272105399367
Origional Values: [array([339.101], dtype=object), array([306.48], dtype=object), array([311.928], dtype=object)]
Predicted Values: [[338.32853426]
[338.93718224]
[315.3006275 ]]
Explained Variance Score: -0.07374831424310835
Maximam Error : 32.45718224316715
Mean Absolute Error : 12.200758494763837
Mean Squared Error : 355.14666624220695
r2 score : -0.7445425826556515
Origional Values: [array([27063.36], dtype=object), array([27048.59], dtype=object), array([26955.193], dtype=object)]
Predicted Values: [[26795.71519377]
[27211.58840464]
[27199.01461459]]
Explained Variance Score: -20.974629117999267
Maximam Error : 267.64480623442796
Mean Absolute Error : 224.82160849067077
Mean Squared Error : 52550.4006546163
r2 score : -21.913028568817
Origional Values: [array([799.195], dtype=object), array([756.875], dtype=object), array([765.729], dtype=object)]
Predicted Values: [[790.89539127]
[791.02934619]
[790.70481148]]
Explained Variance Score: -0.0015014996406306214
Maximam Error : 34.15434619021107
Mean Absolute Error : 22.476588801192975
Mean Squared Error : 619.7313426521844
r2 score : -0.8658183847701963
Origional Values: [array([78.992], dtype=object), array([79.685], dtype=object), array([76.874], dtype=object)]
Predicted Values: [[90.40272775]
[84.60813707]
[85.06880603]]
Explained Variance Score: -3.9063819732520075
Maximam Error : 11.410727748807929
Mean Absolute Error : 8.17622361510334
Mean Squared Error : 73.86561073254218
r2 score : -50.662727140344465
Origional Values: [array([47.789], dtype=object), array([47.693], dtype=object), array([50.899], dtype=object)]
Predicted Values: [[48.11547175]
[45.7036284 ]
[45.60446911]]
Explained Variance Score: -1.3989716444479656
Maximam Error : 5.294530893613185
Mean Absolute Error : 2.5367914124625486
Mean Squared Error : 10.698746843652986
r2 score : -3.824144187406577
Origional Values: [array([3.751], dtype=object), array([3.855], dtype=object), array([4.131], dtype=object)]
Predicted Values: [[3.59677167]
[3.71071667]
[3.78668 ]]
Explained Variance Score: 0.6704794688110258
Maximam Error : 0.3443199996044739
Mean Absolute Error : 0.21427722007703887
Mean Squared Error : 0.054386773127596995
r2 score : -1.1153754587382974
Origional Values: [array([1866.712], dtype=object), array([2030.043], dtype=object), array([2043.407], dtype=object)]
Predicted Values: [[1938.30715753]
[1989.42580163]
[2158.60010227]]
Explained Variance Score: 0.33244795296285656
Maximam Error : 115.19310227121628
Mean Absolute Error : 75.80181939000363
Mean Squared Error : 6681.69139856066
r2 score : -0.03544427044908027
Origional Values: [array([440.104], dtype=object), array([477.653], dtype=object), array([479.376], dtype=object)]
Predicted Values: [[453.29179683]
[456.50903277]
[495.88451923]]
Explained Variance Score: 0.11768434988705423
Maximam Error : 21.143967230450244
Mean Absolute Error : 16.946761096290288
Mean Squared Error : 297.83884753932756
r2 score : 0.09293373599888732
Origional Values: [array([51.398], dtype=object), array([51.303], dtype=object), array([52.75], dtype=object)]
Predicted Values: [[51.5704614 ]
[50.61566787]
[50.52603804]]
Explained Variance Score: -1.249786444796546
Maximam Error : 2.223961959463338
Mean Absolute Error : 1.0279184974855606
Mean Squared Error : 1.8160583971355153
r2 score : -3.1581315198875206
Origional Values: [array([71.43], dtype=object), array([69.624], dtype=object), array([70.97], dtype=object)]
Predicted Values: [[77.5897621 ]
[73.72325097]
[72.86802511]]
Explained Variance Score: -4.1568119355111595
Maximam Error : 6.159762099575204
Mean Absolute Error : 4.052346059084779
Mean Squared Error : 19.449675643935922
r2 score : -32.12179198513466
Origional Values: [array([360.773], dtype=object), array([340.683], dtype=object), array([346.837], dtype=object)]
Predicted Values: [[388.44266265]
[365.26772988]
[349.30447131]]
Explained Variance Score: -0.7836319195243817
Maximam Error : 27.66966264846684
Mean Absolute Error : 18.240621276877693
Mean Squared Error : 458.70252959167084
r2 score : -5.494219429875369
Origional Values: [array([1270.62], dtype=object), array([1235.109], dtype=object), array([1202.308], dtype=object)]
Predicted Values: [[1214.91070582]
[1222.94164957]
[1209.69193561]]
Explained Variance Score: 0.10631261201742104
Maximam Error : 55.709294176236654
Mean Absolute Error : 25.08686007260606
Mean Squared Error : 1102.0307930841122
r2 score : -0.41619550152569285
Origional Values: [array([250.07], dtype=object), array([230.596], dtype=object), array([226.456], dtype=object)]
Predicted Values: [[232.03566357]
[240.71455977]
[229.5120129 ]]
Explained Variance Score: -0.3493413509969223
Maximam Error : 18.034336428156934
Mean Absolute Error : 10.402969701988306
Mean Squared Error : 145.65391906189598
r2 score : -0.3740975053407323
Origional Values: [array([624.611], dtype=object), array([616.627], dtype=object), array([632.478], dtype=object)]
Predicted Values: [[638.18741659]
[636.69141811]
[629.25427497]]
Explained Variance Score: -1.299559012406109
Maximam Error : 20.064418109431926
Mean Absolute Error : 12.288186575687329
Mean Squared Error : 199.09745485636645
r2 score : -3.754400245072442
Origional Values: [array([7.383], dtype=object), array([7.122], dtype=object), array([7.131], dtype=object)]
Predicted Values: [[8.38291526]
[8.40440103]
[8.42347512]]
Explained Variance Score: -0.25651806305133174
Maximam Error : 1.292475124757324
Mean Absolute Error : 1.191597138091674
Mean Squared Error : 1.438291624842961
r2 score : -97.28424387337442
Origional Values: [array([10.531], dtype=object), array([9.838], dtype=object), array([10.284], dtype=object)]
Predicted Values: [[11.8806911 ]
[11.85240918]
[11.81709501]]
Explained Variance Score: 0.044615910733157405
Maximam Error : 2.0144091846837338
Mean Absolute Error : 1.6323984314672835
Mean Squared Error : 2.7432969127111804
r2 score : -32.35657860773347
Origional Values: [array([10.587], dtype=object), array([10.017], dtype=object), array([9.593], dtype=object)]
Predicted Values: [[11.31784978]
[11.05474208]
[10.81324226]]
Explained Variance Score: 0.7541426557505558
Maximam Error : 1.22024226448152
Mean Absolute Error : 0.9962780417207545
Mean Squared Error : 1.0333470706426386
r2 score : -5.230353635385629
Origional Values: [array([224.23], dtype=object), array([236.862], dtype=object), array([238.435], dtype=object)]
Predicted Values: [[202.29272772]
[210.18340781]
[215.75029254]]
Explained Variance Score: 0.8928330380462017
Maximam Error : 26.678592186916234
Mean Absolute Error : 23.766857310964223
Mean Squared Error : 569.1957163258238
r2 score : -13.080337121155413
Origional Values: [array([3.67], dtype=object), array([3.61], dtype=object), array([3.121], dtype=object)]
Predicted Values: [[3.84436549]
[3.75101279]
[3.72578233]]
Explained Variance Score: 0.2621999451740453
Maximam Error : 0.6047823276113342
Mean Absolute Error : 0.30672020240098546
Mean Squared Error : 0.13868319827556905
r2 score : -1.293876712355174
Origional Values: [array([459.256], dtype=object), array([444.256], dtype=object), array([453.009], dtype=object)]
Predicted Values: [[467.73641643]
[461.07199619]
[450.29628167]]
Explained Variance Score: -0.691342226633396
Maximam Error : 16.815996187515623
Mean Absolute Error : 9.336376982793658
Mean Squared Error : 120.68467710783074
r2 score : -2.188592169374756
Origional Values: [array([55.286], dtype=object), array([57.194], dtype=object), array([59.222], dtype=object)]
Predicted Values: [[58.11444453]
[57.47246855]
[59.45128952]]
Explained Variance Score: 0.4295458525531851
Maximam Error : 2.8284445322112504
Mean Absolute Error : 1.1120675321500666
Mean Squared Error : 2.7100722953543315
r2 score : -0.049270368216060234
Origional Values: [array([183.301], dtype=object), array([175.71], dtype=object), array([181.652], dtype=object)]
Predicted Values: [[190.97735506]
[190.9752015 ]
[191.02660934]]
Explained Variance Score: 0.004977657571040628
Maximam Error : 15.265201500413752
Mean Absolute Error : 10.772055301941966
Mean Squared Error : 126.61203474141699
r2 score : -10.913331388664854
Origional Values: [array([33.744], dtype=object), array([34.018], dtype=object), array([33.943], dtype=object)]
Predicted Values: [[33.7387176 ]
[33.82638415]
[33.96209396]]
Explained Variance Score: 0.33739270324211423
Maximam Error : 0.191615845138827
Mean Absolute Error : 0.07199740257293523
Mean Squared Error : 0.01236970508968619
r2 score : 0.07460103899207338
Origional Values: [array([51.011], dtype=object), array([52.21], dtype=object), array([52.02], dtype=object)]
Predicted Values: [[52.46217022]
[52.44961746]
[52.42072926]]
Explained Variance Score: -0.04232364557258328
Maximam Error : 1.4511702176579035
Mean Absolute Error : 0.6971723129145863
Mean Squared Error : 0.774631822584911
r2 score : -1.7978717252408973
Origional Values: [array([44.85], dtype=object), array([48.151], dtype=object), array([49.194], dtype=object)]
Predicted Values: [[45.67102387]
[46.57239653]
[49.72100093]]
Explained Variance Score: 0.6668837454525647
Maximam Error : 1.5786034711702257
Mean Absolute Error : 0.9755427566490363
Mean Squared Error : 1.147933030149481
r2 score : 0.665160624632259
Origional Values: [array([96.2], dtype=object), array([102.035], dtype=object), array([110.825], dtype=object)]
Predicted Values: [[ 87.15872385]
[ 96.24851043]
[101.65402456]]
Explained Variance Score: 0.9321492317134044
Maximam Error : 9.170975439174882
Mean Absolute Error : 7.999580389549801
Mean Squared Error : 66.44497553729185
r2 score : -0.8388720603785638
Origional Values: [array([293.7], dtype=object), array([280.762], dtype=object), array([283.693], dtype=object)]
Predicted Values: [[305.43680974]
[306.64722926]
[310.1569576 ]]
Explained Variance Score: -0.511652809688615
Maximam Error : 26.463957597421143
Mean Absolute Error : 21.361998864815877
Mean Squared Error : 502.712949471259
r2 score : -15.38553186310217
Origional Values: [array([46.816], dtype=object), array([46.064], dtype=object), array([50.077], dtype=object)]
Predicted Values: [[48.28193024]
[46.76756376]
[46.18772487]]
Explained Variance Score: -0.8441977816420694
Maximam Error : 3.8892751341628227
Mean Absolute Error : 2.0195897106330896
Mean Squared Error : 5.923471498046663
r2 score : -0.9525214511367923
Origional Values: [array([70.074], dtype=object), array([69.792], dtype=object), array([70.978], dtype=object)]
Predicted Values: [[80.00211036]
[72.83406039]
[72.62875975]]
Explained Variance Score: -50.17270547313776
Maximam Error : 9.928110358370915
Mean Absolute Error : 4.873643499318149
Mean Squared Error : 36.848838151076485
r2 score : -142.9822689176447
Origional Values: [array([1531.207], dtype=object), array([1511.148], dtype=object), array([1487.625], dtype=object)]
Predicted Values: [[1549.85289832]
[1550.71232455]
[1551.20139461]]
Explained Variance Score: -0.06228318342390016
Maximam Error : 63.57639461499775
Mean Absolute Error : 40.59553916015807
Mean Squared Error : 1984.9877510885601
r2 score : -5.257216568683594
Origional Values: [array([478.436], dtype=object), array([515.782], dtype=object), array([541.376], dtype=object)]
Predicted Values: [[497.96448129]
[506.43996154]
[545.21556917]]
Explained Variance Score: 0.7914893111694813
Maximam Error : 19.528481294697542
Mean Absolute Error : 10.903362975501807
Mean Squared Error : 161.1258519108654
r2 score : 0.7587623426642738
Origional Values: [array([122.854], dtype=object), array([124.33], dtype=object), array([122.394], dtype=object)]
Predicted Values: [[122.06335331]
[125.11127208]
[126.33110073]]
Explained Variance Score: -4.666379526809391
Maximam Error : 3.9371007342474797
Mean Absolute Error : 1.8363398321160578
Mean Squared Error : 5.578756810199242
r2 score : -7.179632849732544
Origional Values: [array([31.057], dtype=object), array([28.94], dtype=object), array([29.152], dtype=object)]
Predicted Values: [[31.21144785]
[31.40170507]
[30.02234019]]
Explained Variance Score: -0.026082743970809785
Maximam Error : 2.461705071920388
Mean Absolute Error : 1.1621643692358496
Mean Squared Error : 2.2804460144021896
r2 score : -1.5165368790132883
Origional Values: [array([14.389], dtype=object), array([12.786], dtype=object), array([12.892], dtype=object)]
Predicted Values: [[15.81455319]
[15.93629078]
[16.25991493]]
Explained Variance Score: -0.40917771017434035
Maximam Error : 3.367914932422689
Mean Absolute Error : 2.6479196347439427
Mean Squared Error : 7.766461634119715
r2 score : -13.496115956036299
Origional Values: [array([425.375], dtype=object), array([439.015], dtype=object), array([414.513], dtype=object)]
Predicted Values: [[409.45126884]
[412.81770051]
[417.37397437]]
Explained Variance Score: -0.4405379534613527
Maximam Error : 26.197299486561406
Mean Absolute Error : 14.994001671964705
Mean Squared Error : 316.0162962232546
r2 score : -2.1448557333673617
Origional Values: [array([251.47], dtype=object), array([248.941], dtype=object), array([263.328], dtype=object)]
Predicted Values: [[273.05942974]
[249.48293458]
[247.27263816]]
Explained Variance Score: -5.032867779536096
Maximam Error : 21.589429735248785
Mean Absolute Error : 12.728908718617362
Mean Squared Error : 241.39060438278224
r2 score : -5.137157201654763
Origional Values: [array([45.003], dtype=object), array([45.367], dtype=object), array([43.803], dtype=object)]
Predicted Values: [[48.21799266]
[47.88698399]
[48.05340272]]
Explained Variance Score: -0.13210298534890597
Maximam Error : 4.250402715056659
Mean Absolute Error : 3.328459789319846
Mean Squared Error : 11.584140124685698
r2 score : -24.943728828945847
Origional Values: [array([50.448], dtype=object), array([46.045], dtype=object), array([44.867], dtype=object)]
Predicted Values: [[50.90337232]
[51.13489375]
[49.37732677]]
Explained Variance Score: 0.2631717325678813
Maximam Error : 5.089893746285718
Mean Absolute Error : 3.351864279243062
Mean Squared Error : 15.485809964135607
r2 score : -1.6842806216693398
Origional Values: [array([264.26], dtype=object), array([266.809], dtype=object), array([268.224], dtype=object)]
Predicted Values: [[269.53152801]
[269.67775598]
[270.09282849]]
Explained Variance Score: 0.242076917891339
Maximam Error : 5.271528010417455
Mean Absolute Error : 3.3363708267842944
Mean Squared Error : 13.170429457050275
r2 score : -3.895479575470163
Origional Values: [array([262.577], dtype=object), array([259.148], dtype=object), array([267.676], dtype=object)]
Predicted Values: [[257.06035784]
[266.90959242]
[263.59102846]]
Explained Variance Score: -1.8845847900605395
Maximam Error : 7.761592415372263
Mean Absolute Error : 5.7877353709863355
Mean Squared Error : 35.787550001569635
r2 score : -1.9152286763782809
Origional Values: [array([25.545], dtype=object), array([26.987], dtype=object), array([27.462], dtype=object)]
Predicted Values: [[25.48821598]
[25.41730916]
[26.38190944]]
Explained Variance Score: 0.40203600496841607
Maximam Error : 1.5696908366376903
Mean Absolute Error : 0.9021884712980727
Mean Squared Error : 1.2112497864855385
r2 score : -0.8229883750755242
Origional Values: [array([297.813], dtype=object), array([321.668], dtype=object), array([336.883], dtype=object)]
Predicted Values: [[308.57543712]
[300.49437361]
[316.78832762]]
Explained Variance Score: 0.1520332110954965
Maximam Error : 21.17362638989482
Mean Absolute Error : 17.343578627402866
Mean Squared Error : 322.64945502789277
r2 score : -0.24788029853993243
Origional Values: [array([482.462], dtype=object), array([444.989], dtype=object), array([430.787], dtype=object)]
Predicted Values: [[496.67572084]
[486.12736729]
[458.91932403]]
Explained Variance Score: 0.7456119686417608
Maximam Error : 41.13836729125137
Mean Absolute Error : 27.828137387289832
Mean Squared Error : 895.274259640577
r2 score : -0.8842465928042724
Origional Values: [array([5146.637], dtype=object), array([5155.535], dtype=object), array([5020.004], dtype=object)]
Predicted Values: [[5008.29827799]
[5114.73565636]
[5121.80821902]]
Explained Variance Score: -1.537959362484603
Maximam Error : 138.3387220059949
Mean Absolute Error : 93.64742822067667
Mean Squared Error : 10388.762485753005
r2 score : -1.7113896024965198
Origional Values: [array([133.605], dtype=object), array([147.779], dtype=object), array([173.294], dtype=object)]
Predicted Values: [[132.86359085]
[137.22667452]
[149.88631589]]
Explained Variance Score: 0.6805803575562507
Maximam Error : 23.407684113929378
Mean Absolute Error : 11.567139581216756
Mean Squared Error : 219.94031201959896
r2 score : 0.18444444059411047
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [[31984.90600637]
[32520.7585233 ]
[32555.40789394]]
Explained Variance Score: -132.9562192489812
Maximam Error : 302.8939936349161
Mean Absolute Error : 258.4201369571929
Mean Squared Error : 69015.51142951909
r2 score : -139.45051710028866
i=0
VVIP2=list()
while i<len(CO2_PROD_FINAL.T):
series=CO2_PROD_FINAL[CO2_PROD_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = RandomForestRegressor()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP2.append(pre)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([184.817], dtype=object), array([188.737], dtype=object), array([195.027], dtype=object)]
Predicted Values: [184.16118 184.16118 184.16118]
Explained Variance Score: 0.0
Maximam Error : 10.865819999999957
Mean Absolute Error : 5.3658199999999665
Mean Squared Error : 46.47809093906623
r2 score : -1.6279495500638776
Origional Values: [array([395.402], dtype=object), array([385.987], dtype=object), array([392.488], dtype=object)]
Predicted Values: [398.35561 398.35561 396.46035]
Explained Variance Score: -0.14907546450578746
Maximam Error : 12.36860999999982
Mean Absolute Error : 6.4315233333334545
Mean Squared Error : 59.161963295566714
r2 score : -2.8197315115720993
Origional Values: [array([66.578], dtype=object), array([62.77], dtype=object), array([64.256], dtype=object)]
Predicted Values: [68.75184 66.93376 68.75184]
Explained Variance Score: 0.5718812573549377
Maximam Error : 4.49583999999993
Mean Absolute Error : 3.6111466666666345
Mean Squared Error : 14.09168499626648
r2 score : -4.7385020597676855
Origional Values: [array([101.642], dtype=object), array([95.113], dtype=object), array([101.301], dtype=object)]
Predicted Values: [105.31712 105.31712 105.31712]
Explained Variance Score: 0.0
Maximam Error : 10.204119999999875
Mean Absolute Error : 5.965119999999875
Mean Squared Error : 44.586597281065174
r2 score : -3.9518981667802926
Origional Values: [array([460.643], dtype=object), array([484.935], dtype=object), array([461.154], dtype=object)]
Predicted Values: [421.20008 421.20008 421.20008]
Explained Variance Score: 0.0
Maximam Error : 63.73491999999993
Mean Absolute Error : 47.710586666666586
Mean Squared Error : 2404.733229633059
r2 score : -17.723617990796484
Origional Values: [array([7.433], dtype=object), array([7.282], dtype=object), array([6.71], dtype=object)]
Predicted Values: [7.6557 7.62205 7.62205]
Explained Variance Score: 0.06480422566843302
Maximam Error : 0.9120499999999927
Mean Absolute Error : 0.49159999999999293
Mean Squared Error : 0.3323548316666596
r2 score : -2.427461327536784
Origional Values: [array([39.502], dtype=object), array([41.429], dtype=object), array([43.564], dtype=object)]
Predicted Values: [48.28556 46.01296 46.01296]
Explained Variance Score: -1.5158863589370521
Maximam Error : 8.783560000000001
Mean Absolute Error : 5.272159999999985
Mean Squared Error : 34.72034021226657
r2 score : -11.61467204678555
Origional Values: [array([5.997], dtype=object), array([6.969], dtype=object), array([8.821], dtype=object)]
Predicted Values: [5.57216 5.57216 5.57216]
Explained Variance Score: 0.0
Maximam Error : 3.248839999999994
Mean Absolute Error : 1.6901733333333278
Mean Squared Error : 4.228870785599981
r2 score : -2.081852030176678
Origional Values: [array([555.889], dtype=object), array([557.952], dtype=object), array([556.444], dtype=object)]
Predicted Values: [569.37147 543.16961 534.81729]
Explained Variance Score: -302.94617117232826
Maximam Error : 21.626709999999548
Mean Absolute Error : 16.630523333332917
Mean Squared Error : 289.3368789456858
r2 score : -379.81453981688486
Origional Values: [array([89.437], dtype=object), array([82.263], dtype=object), array([86.705], dtype=object)]
Predicted Values: [84.01711 84.01711 84.01711]
Explained Variance Score: 0.0
Maximam Error : 5.419889999999967
Mean Absolute Error : 3.2872966666666534
Mean Squared Error : 13.22562071876652
r2 score : -0.5132007518814923
Origional Values: [array([71.888], dtype=object), array([72.416], dtype=object), array([70.399], dtype=object)]
Predicted Values: [71.24511 69.97225 69.9334 ]
Explained Variance Score: -0.09497158466067379
Maximam Error : 2.443749999999909
Mean Absolute Error : 1.1840799999999045
Mean Squared Error : 2.2006683248664487
r2 score : -2.0172805562721146
Origional Values: [array([9319.037], dtype=object), array([9264.018], dtype=object), array([9280.847], dtype=object)]
Predicted Values: [8720.16832 8720.16832 8720.16832]
Explained Variance Score: 0.0
Maximam Error : 598.8686800000105
Mean Absolute Error : 567.7990133333436
Mean Squared Error : 322925.584175874
r2 score : -608.449213488622
Origional Values: [array([79.214], dtype=object), array([82.854], dtype=object), array([82.755], dtype=object)]
Predicted Values: [69.13308 69.13308 69.13308]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 13.720920000000078
Mean Absolute Error : 12.474586666666744
Mean Squared Error : 158.48176605973524
r2 score : -54.28844720074996
Origional Values: [array([8.296], dtype=object), array([8.43], dtype=object), array([8.003], dtype=object)]
Predicted Values: [7.54981 7.54981 7.54981]
Explained Variance Score: 0.0
Maximam Error : 0.8801899999999945
Mean Absolute Error : 0.6931899999999945
Mean Squared Error : 0.5123050427666589
r2 score : -15.113937473001954
Origional Values: [array([15.792], dtype=object), array([15.052], dtype=object), array([15.269], dtype=object)]
Predicted Values: [17.01857 17.01857 17.01857]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 1.9665699999999653
Mean Absolute Error : 1.6475699999999651
Mean Squared Error : 2.8109555715665517
r2 score : -28.138534497210326
Origional Values: [array([7.534], dtype=object), array([7.324], dtype=object), array([7.325], dtype=object)]
Predicted Values: [8.90242 8.5191 8.5191 ]
Explained Variance Score: 0.31160961472742255
Maximam Error : 1.368420000000003
Mean Absolute Error : 1.2525400000000058
Mean Squared Error : 1.5755707054666805
r2 score : -160.53808695632534
Origional Values: [array([101.372], dtype=object), array([98.856], dtype=object), array([99.275], dtype=object)]
Predicted Values: [110.83714 110.83714 110.83714]
Explained Variance Score: 0.0
Maximam Error : 11.981140000000082
Mean Absolute Error : 11.002806666666743
Mean Squared Error : 122.27322409960168
r2 score : -99.92967135565326
Origional Values: [array([74.767], dtype=object), array([66.902], dtype=object), array([64.912], dtype=object)]
Predicted Values: [85.15941 85.15941 85.15941]
Explained Variance Score: 0.0
Maximam Error : 20.247409999999988
Mean Absolute Error : 16.29907666666666
Mean Squared Error : 283.76427240809977
r2 score : -14.673797960240435
Origional Values: [array([19.019], dtype=object), array([17.642], dtype=object), array([15.602], dtype=object)]
Predicted Values: [16.16509 17.62018 16.26461]
Explained Variance Score: -0.17602009377892625
Maximam Error : 2.8539099999999955
Mean Absolute Error : 1.179446666666666
Mean Squared Error : 2.861443470866648
r2 score : -0.4522130361553891
Origional Values: [array([52.903], dtype=object), array([48.379], dtype=object), array([45.243], dtype=object)]
Predicted Values: [65.71041 65.71041 65.71041]
Explained Variance Score: 0.0
Maximam Error : 20.46741000000008
Mean Absolute Error : 16.868743333333416
Mean Squared Error : 294.4407985347694
r2 score : -28.78271863003515
Origional Values: [array([339.101], dtype=object), array([306.48], dtype=object), array([311.928], dtype=object)]
Predicted Values: [345.18539 345.18539 345.18539]
Explained Variance Score: 0.0
Maximam Error : 38.70538999999968
Mean Absolute Error : 26.015723333333028
Mean Squared Error : 880.3936687787506
r2 score : -3.3246477882957404
Origional Values: [array([27063.36], dtype=object), array([27048.59], dtype=object), array([26955.193], dtype=object)]
Predicted Values: [26298.18549 26298.18549 26298.18549]
Explained Variance Score: 0.0
Maximam Error : 765.1745099999753
Mean Absolute Error : 724.1955099999747
Mean Squared Error : 526752.6091927901
r2 score : -228.67470148247196
Origional Values: [array([799.195], dtype=object), array([756.875], dtype=object), array([765.729], dtype=object)]
Predicted Values: [783.79406 806.98524 791.82525]
Explained Variance Score: -1.204628325699332
Maximam Error : 50.110239999999294
Mean Absolute Error : 30.535809999999817
Mean Squared Error : 1143.0797899345425
r2 score : -2.441457839120647
Origional Values: [array([78.992], dtype=object), array([79.685], dtype=object), array([76.874], dtype=object)]
Predicted Values: [95.53924 95.53924 95.53924]
Explained Variance Score: 0.0
Maximam Error : 18.665239999999955
Mean Absolute Error : 17.02223999999995
Mean Squared Error : 291.1864206175983
r2 score : -202.6601937782802
Origional Values: [array([47.789], dtype=object), array([47.693], dtype=object), array([50.899], dtype=object)]
Predicted Values: [51.81429 51.81429 51.81429]
Explained Variance Score: 0.0
Maximam Error : 4.1212899999999735
Mean Absolute Error : 3.0206233333333046
Mean Squared Error : 11.341915544099828
r2 score : -4.114153717786593
Origional Values: [array([3.751], dtype=object), array([3.855], dtype=object), array([4.131], dtype=object)]
Predicted Values: [3.59087 3.63521 3.73789]
Explained Variance Score: 0.6202166954777797
Maximam Error : 0.3931100000000036
Mean Absolute Error : 0.2576766666666665
Mean Squared Error : 0.07616157770000054
r2 score : -1.9623072504667558
Origional Values: [array([1866.712], dtype=object), array([2030.043], dtype=object), array([2043.407], dtype=object)]
Predicted Values: [1766.59244 1766.59244 1766.59244]
Explained Variance Score: 0.0
Maximam Error : 276.81456000000094
Mean Absolute Error : 213.46156000000096
Mean Squared Error : 52018.808162300666
r2 score : -7.061218882219988
Origional Values: [array([440.104], dtype=object), array([477.653], dtype=object), array([479.376], dtype=object)]
Predicted Values: [426.59976 426.59976 426.59976]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 52.77624000000014
Mean Absolute Error : 39.11124000000016
Mean Squared Error : 1858.0431070042796
r2 score : -4.658658141298543
Origional Values: [array([51.398], dtype=object), array([51.303], dtype=object), array([52.75], dtype=object)]
Predicted Values: [52.90482 52.90482 52.90482]
Explained Variance Score: 0.0
Maximam Error : 1.6018199999999467
Mean Absolute Error : 1.0878199999999438
Mean Squared Error : 1.6201010190665446
r2 score : -2.7094584201742515
Origional Values: [array([71.43], dtype=object), array([69.624], dtype=object), array([70.97], dtype=object)]
Predicted Values: [76.16022 72.4754 72.64649]
Explained Variance Score: -1.6936186011529912
Maximam Error : 4.730220000000131
Mean Absolute Error : 3.0860366666667383
Mean Squared Error : 11.105360642833872
r2 score : -17.911854977207692
Origional Values: [array([360.773], dtype=object), array([340.683], dtype=object), array([346.837], dtype=object)]
Predicted Values: [407.00657 407.00657 407.00657]
Explained Variance Score: 0.0
Maximam Error : 66.32356999999928
Mean Absolute Error : 57.57556999999927
Mean Squared Error : 3385.5786954914824
r2 score : -46.932351638010516
Origional Values: [array([1270.62], dtype=object), array([1235.109], dtype=object), array([1202.308], dtype=object)]
Predicted Values: [1189.57125 1189.57125 1203.52941]
Explained Variance Score: -0.45868192907030503
Maximam Error : 81.04875000000197
Mean Absolute Error : 42.60263666666742
Mean Squared Error : 2881.3594646711995
r2 score : -2.7027715901894793
Origional Values: [array([250.07], dtype=object), array([230.596], dtype=object), array([226.456], dtype=object)]
Predicted Values: [227.92826 227.92826 212.63634]
Explained Variance Score: 0.39951713906703923
Maximam Error : 22.14173999999977
Mean Absolute Error : 12.876379999999804
Mean Squared Error : 229.4521631502616
r2 score : -1.1646492384858251
Origional Values: [array([624.611], dtype=object), array([616.627], dtype=object), array([632.478], dtype=object)]
Predicted Values: [623.42825 623.42825 622.57337]
Explained Variance Score: -0.11147532166946528
Maximam Error : 9.904629999999429
Mean Absolute Error : 5.962876666666489
Mean Squared Error : 48.585864853962015
r2 score : -0.16021898891364472
Origional Values: [array([7.383], dtype=object), array([7.122], dtype=object), array([7.131], dtype=object)]
Predicted Values: [8.50743 8.50743 8.50743]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 1.3854299999999906
Mean Absolute Error : 1.2954299999999905
Mean Squared Error : 1.6927728848999752
r2 score : -114.67397054120374
Origional Values: [array([10.531], dtype=object), array([9.838], dtype=object), array([10.284], dtype=object)]
Predicted Values: [11.64332 11.64332 11.64332]
Explained Variance Score: 0.0
Maximam Error : 1.8053200000000071
Mean Absolute Error : 1.4256533333333394
Mean Squared Error : 2.114728982400018
r2 score : -24.71363063495891
Origional Values: [array([10.587], dtype=object), array([10.017], dtype=object), array([9.593], dtype=object)]
Predicted Values: [11.34323 11.48008 11.48008]
Explained Variance Score: -0.3118636716258665
Maximam Error : 1.887080000000001
Mean Absolute Error : 1.3687966666666689
Mean Squared Error : 2.091185941900005
r2 score : -11.608375545383202
Origional Values: [array([224.23], dtype=object), array([236.862], dtype=object), array([238.435], dtype=object)]
Predicted Values: [203.02042 203.02042 203.02042]
Explained Variance Score: 2.220446049250313e-16
Maximam Error : 35.4145799999998
Mean Absolute Error : 30.15524666666646
Mean Squared Error : 949.7637657497211
r2 score : -22.494544360834702
Origional Values: [array([3.67], dtype=object), array([3.61], dtype=object), array([3.121], dtype=object)]
Predicted Values: [3.87089 3.88925 3.63721]
Explained Variance Score: 0.7027926942854601
Maximam Error : 0.5162100000000027
Mean Absolute Error : 0.33211666666666523
Mean Squared Error : 0.12827003956666647
r2 score : -1.1216388164786548
Origional Values: [array([459.256], dtype=object), array([444.256], dtype=object), array([453.009], dtype=object)]
Predicted Values: [464.34972 464.34972 444.50713]
Explained Variance Score: -2.6036512741618623
Maximam Error : 20.09372000000053
Mean Absolute Error : 11.229770000000258
Mean Squared Error : 167.32845345790727
r2 score : -3.420960549389016
Origional Values: [array([55.286], dtype=object), array([57.194], dtype=object), array([59.222], dtype=object)]
Predicted Values: [55.29574 55.29574 55.29574]
Explained Variance Score: 0.0
Maximam Error : 3.9262600000000347
Mean Absolute Error : 1.9447533333333453
Mean Squared Error : 6.3396678276001355
r2 score : -1.454556510258623
Origional Values: [array([183.301], dtype=object), array([175.71], dtype=object), array([181.652], dtype=object)]
Predicted Values: [187.15228 187.15228 187.15228]
Explained Variance Score: 0.0
Maximam Error : 11.442280000000096
Mean Absolute Error : 6.93128000000011
Mean Squared Error : 58.67040310506814
r2 score : -4.520485918457373
Origional Values: [array([33.744], dtype=object), array([34.018], dtype=object), array([33.943], dtype=object)]
Predicted Values: [32.76926 32.76926 33.44984]
Explained Variance Score: -6.297438386727057
Maximam Error : 1.248740000000005
Mean Absolute Error : 0.9055466666666661
Mean Squared Error : 0.9175588136000034
r2 score : -67.64415655932586
Origional Values: [array([51.011], dtype=object), array([52.21], dtype=object), array([52.02], dtype=object)]
Predicted Values: [52.68741 54.37642 51.3775 ]
Explained Variance Score: -4.420813279251311
Maximam Error : 2.166419999999981
Mean Absolute Error : 1.4951099999999566
Mean Squared Error : 2.6388441181665256
r2 score : -8.531169686392623
Origional Values: [array([44.85], dtype=object), array([48.151], dtype=object), array([49.194], dtype=object)]
Predicted Values: [43.73543 43.73543 43.73543]
Explained Variance Score: 0.0
Maximam Error : 5.458569999999931
Mean Absolute Error : 3.6629033333332637
Mean Squared Error : 16.845170384899493
r2 score : -3.91354999072414
Origional Values: [array([96.2], dtype=object), array([102.035], dtype=object), array([110.825], dtype=object)]
Predicted Values: [85.50861 85.50861 85.50861]
Explained Variance Score: 0.0
Maximam Error : 25.31639000000007
Mean Absolute Error : 17.511390000000066
Mean Squared Error : 342.78232973210237
r2 score : -8.48653895706628
Origional Values: [array([293.7], dtype=object), array([280.762], dtype=object), array([283.693], dtype=object)]
Predicted Values: [307.77616 307.40614 307.40614]
Explained Variance Score: 0.060503504356338444
Maximam Error : 26.64414000000039
Mean Absolute Error : 21.47781333333353
Mean Squared Error : 490.12049511494473
r2 score : -14.975090750918637
Origional Values: [array([46.816], dtype=object), array([46.064], dtype=object), array([50.077], dtype=object)]
Predicted Values: [49.37959 49.37959 49.37959]
Explained Variance Score: 0.0
Maximam Error : 3.3155900000000145
Mean Absolute Error : 2.19219666666667
Mean Squared Error : 6.017170481433378
r2 score : -0.9834069335895399
Origional Values: [array([70.074], dtype=object), array([69.792], dtype=object), array([70.978], dtype=object)]
Predicted Values: [77.05527 80.32464 80.32464]
Explained Variance Score: -7.515388164730977
Maximam Error : 10.53264
Mean Absolute Error : 8.95351666666671
Mean Squared Error : 82.34477182403396
r2 score : -320.75199207424095
Origional Values: [array([1531.207], dtype=object), array([1511.148], dtype=object), array([1487.625], dtype=object)]
Predicted Values: [1481.09284 1596.68923 1564.21943]
Explained Variance Score: -11.096833532804009
Maximam Error : 85.54123000000186
Mean Absolute Error : 70.74994000000167
Mean Squared Error : 5231.81258981472
r2 score : -15.492083844488949
Origional Values: [array([478.436], dtype=object), array([515.782], dtype=object), array([541.376], dtype=object)]
Predicted Values: [459.95027 459.95027 459.95027]
Explained Variance Score: 0.0
Maximam Error : 81.42572999999959
Mean Absolute Error : 51.91439666666628
Mean Squared Error : 3363.017931486193
r2 score : -4.035111111893899
Origional Values: [array([122.854], dtype=object), array([124.33], dtype=object), array([122.394], dtype=object)]
Predicted Values: [121.01575 119.72778 119.72778]
Explained Variance Score: -0.9668663235844024
Maximam Error : 4.602220000000031
Mean Absolute Error : 3.0355633333333194
Mean Squared Error : 10.55610702643336
r2 score : -14.477476924760062
Origional Values: [array([31.057], dtype=object), array([28.94], dtype=object), array([29.152], dtype=object)]
Predicted Values: [33.39805 33.39805 33.39805]
Explained Variance Score: 0.0
Maximam Error : 4.45804999999994
Mean Absolute Error : 3.6817166666666075
Mean Squared Error : 14.461221835832893
r2 score : -14.958368597910352
Origional Values: [array([14.389], dtype=object), array([12.786], dtype=object), array([12.892], dtype=object)]
Predicted Values: [15.51729 15.51729 15.51729]
Explained Variance Score: 0.0
Maximam Error : 2.731289999999987
Mean Absolute Error : 2.1616233333333206
Mean Squared Error : 5.208376990766612
r2 score : -8.721445924513583
Origional Values: [array([425.375], dtype=object), array([439.015], dtype=object), array([414.513], dtype=object)]
Predicted Values: [401.03619 403.59093 403.59093]
Explained Variance Score: 0.001261352183199893
Maximam Error : 35.424070000000256
Mean Absolute Error : 23.561650000000345
Mean Squared Error : 655.5113402219832
r2 score : -5.5233616785637425
Origional Values: [array([251.47], dtype=object), array([248.941], dtype=object), array([263.328], dtype=object)]
Predicted Values: [282.02405 282.02405 282.02405]
Explained Variance Score: 0.0
Maximam Error : 33.08305000000021
Mean Absolute Error : 27.44438333333356
Mean Squared Error : 792.5268181025122
r2 score : -19.149341278874125
Origional Values: [array([45.003], dtype=object), array([45.367], dtype=object), array([43.803], dtype=object)]
Predicted Values: [52.48505 52.48505 52.48505]
Explained Variance Score: 0.0
Maximam Error : 8.68204999999994
Mean Absolute Error : 7.760716666666606
Mean Squared Error : 60.67523340249905
r2 score : -134.88766926886103
Origional Values: [array([50.448], dtype=object), array([46.045], dtype=object), array([44.867], dtype=object)]
Predicted Values: [50.88075 50.88075 50.88075]
Explained Variance Score: 1.1102230246251565e-16
Maximam Error : 6.013750000000044
Mean Absolute Error : 3.760750000000042
Mean Squared Error : 19.912313229166983
r2 score : -2.451562214533896
Origional Values: [array([264.26], dtype=object), array([266.809], dtype=object), array([268.224], dtype=object)]
Predicted Values: [273.92216 273.48156 273.4644 ]
Explained Variance Score: -0.26134102786934
Maximam Error : 9.662160000000029
Mean Absolute Error : 7.191706666666676
Mean Squared Error : 55.11406165973349
r2 score : -19.486026219289027
Origional Values: [array([262.577], dtype=object), array([259.148], dtype=object), array([267.676], dtype=object)]
Predicted Values: [239.68409 239.68409 239.68409]
Explained Variance Score: 0.0
Maximam Error : 27.991910000000132
Mean Absolute Error : 23.449576666666815
Mean Squared Error : 562.1587154014402
r2 score : -44.79305394592188
Origional Values: [array([25.545], dtype=object), array([26.987], dtype=object), array([27.462], dtype=object)]
Predicted Values: [24.84903 24.84903 24.84903]
Explained Variance Score: 0.0
Maximam Error : 2.6129700000000007
Mean Absolute Error : 1.8156366666666675
Mean Squared Error : 3.9609673942333345
r2 score : -4.961443786662556
Origional Values: [array([297.813], dtype=object), array([321.668], dtype=object), array([336.883], dtype=object)]
Predicted Values: [304.39369 304.39369 304.39369]
Explained Variance Score: 0.0
Maximam Error : 32.489309999999875
Mean Absolute Error : 18.781436666666632
Mean Squared Error : 465.75417704276333
r2 score : -0.8013526830352133
Origional Values: [array([482.462], dtype=object), array([444.989], dtype=object), array([430.787], dtype=object)]
Predicted Values: [503.86969 500.7991 500.7991 ]
Explained Variance Score: 0.12361755481985637
Maximam Error : 70.01209999999986
Mean Absolute Error : 49.07662999999982
Mean Squared Error : 2824.9168665186844
r2 score : -4.945485334103201
Origional Values: [array([5146.637], dtype=object), array([5155.535], dtype=object), array([5020.004], dtype=object)]
Predicted Values: [5313.52099 5313.52099 5313.52099]
Explained Variance Score: 0.0
Maximam Error : 293.5169900000037
Mean Absolute Error : 206.1289900000038
Mean Squared Error : 46320.687524421664
r2 score : -11.089354310144653
Origional Values: [array([133.605], dtype=object), array([147.779], dtype=object), array([173.294], dtype=object)]
Predicted Values: [128.52725 128.61346 128.61346]
Explained Variance Score: 0.0038202230629690304
Maximam Error : 44.68054000000009
Mean Absolute Error : 22.974610000000098
Mean Squared Error : 796.4840410819041
r2 score : -1.9534239617907416
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [31079.577 31079.577 31079.577]
Explained Variance Score: 0.0
Maximam Error : 1248.2229999999836
Mean Absolute Error : 1217.622999999984
Mean Squared Error : 1483097.1567956274
r2 score : -3017.1876257577023
i=0
VVIP3=list()
VVIP4=list()
while i<len(CO2_PROD_FINAL.T):
series=CO2_PROD_FINAL[CO2_PROD_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
# Splitting the data into Train & Test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=3,shuffle=False)
# Fitting the model on Train dataset
Model = MLPRegressor ()
Model = Model.fit(X_train, y_train)
# Predicting and storing results for Test dataset
train_fit = Model.predict(X_train)
test_pred = Model.predict(X_test)
plt.figure(figsize=(12,4))
# Plotting Regression line on Train Dataset
plt.subplot(1,2,1)
plt.plot(y_train, color='gray')
plt.plot(y_train.index,train_fit, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title("Train Dataset")
# Plotting Regression line on Test Dataset
plt.subplot(1,2,2)
plt.plot(y_test, color='gray')
plt.plot(y_test.index,test_pred, color='blue', linewidth=2)
plt.xlabel('Timeline')
plt.ylabel('CO2(Millions of metric tons)')
plt.title(str(X.columns.values)+"Test Dataset")
plt.legend(['Origional','Predicted'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
pre=test_pred
y_test=y_test
y_test=np.array(y_test)
VVIP3.append(pre)
VVIP4.append(y_test)
exp_rf=explained_variance_score(y_test,pre)
mxer_rf=max_error(y_test,pre)
mae_rf=mean_absolute_error(y_test,pre)
mse_rf=mean_squared_error(y_test,pre)
r2_rf=r2_score(y_test,pre)
print("Origional Values: " , list(y_test))
print("Predicted Values: " , pre)
print("Explained Variance Score:",exp_rf)
print(" Maximam Error :",mxer_rf)
print(" Mean Absolute Error :",mae_rf)
print(" Mean Squared Error :",mse_rf)
print(" r2 score :",r2_rf)
i=i+1
Origional Values: [array([184.817], dtype=object), array([188.737], dtype=object), array([195.027], dtype=object)]
Predicted Values: [196.70730806 191.73655909 195.7799988 ]
Explained Variance Score: -0.3075711065051856
Maximam Error : 11.890308056113355
Mean Absolute Error : 5.2142886480114425
Mean Squared Error : 50.31459586583872
r2 score : -1.84487200088802
Origional Values: [array([395.402], dtype=object), array([385.987], dtype=object), array([392.488], dtype=object)]
Predicted Values: [401.29880924 398.77156068 389.2918614 ]
Explained Variance Score: -1.7655302127213544
Maximam Error : 12.78456068040606
Mean Absolute Error : 7.292502839278673
Mean Squared Error : 69.477550979123
r2 score : -3.4857468555594684
Origional Values: [array([66.578], dtype=object), array([62.77], dtype=object), array([64.256], dtype=object)]
Predicted Values: [64.8717628 65.81249532 62.11939282]
Explained Variance Score: -1.2424033131171508
Maximam Error : 3.042495319234895
Mean Absolute Error : 2.295113233283722
Mean Squared Error : 5.577704465334226
r2 score : -1.271386890324056
Origional Values: [array([101.642], dtype=object), array([95.113], dtype=object), array([101.301], dtype=object)]
Predicted Values: [100.50539799 99.86938106 93.49064907]
Explained Variance Score: -1.9269767767031403
Maximam Error : 7.810350934096874
Mean Absolute Error : 4.567778001853507
Mean Squared Error : 28.305535538250453
r2 score : -2.1436830368107485
Origional Values: [array([460.643], dtype=object), array([484.935], dtype=object), array([461.154], dtype=object)]
Predicted Values: [454.87422943 485.00806161 510.52790023]
Explained Variance Score: -3.762887544748253
Maximam Error : 49.37390023361655
Mean Absolute Error : 18.405244135493508
Mean Squared Error : 823.6886920367069
r2 score : -5.413365201173452
Origional Values: [array([7.433], dtype=object), array([7.282], dtype=object), array([6.71], dtype=object)]
Predicted Values: [7.94493271 7.77615388 7.6337762 ]
Explained Variance Score: 0.5937884589542702
Maximam Error : 0.9237762035440804
Mean Absolute Error : 0.6432875946809261
Mean Squared Error : 0.45320854045172504
r2 score : -3.673784153875756
Origional Values: [array([39.502], dtype=object), array([41.429], dtype=object), array([43.564], dtype=object)]
Predicted Values: [44.40911154 39.44200412 41.32643168]
Explained Variance Score: -2.981929238112657
Maximam Error : 4.907111540868726
Mean Absolute Error : 3.0438919128900346
Mean Squared Error : 11.011536093405953
r2 score : -3.000736044072026
Origional Values: [array([5.997], dtype=object), array([6.969], dtype=object), array([8.821], dtype=object)]
Predicted Values: [6.07339047 6.32983017 7.24073044]
Explained Variance Score: 0.6645886324661465
Maximam Error : 1.5802695617033145
Mean Absolute Error : 0.76527661794587
Mean Squared Error : 0.9705418192326195
r2 score : 0.29270331783167725
Origional Values: [array([555.889], dtype=object), array([557.952], dtype=object), array([556.444], dtype=object)]
Predicted Values: [553.79559866 562.95254733 565.03946571]
Explained Variance Score: -24.957578142493205
Maximam Error : 8.59546570904422
Mean Absolute Error : 5.229804796130982
Mean Squared Error : 34.42327786370252
r2 score : -44.306650042061975
Origional Values: [array([89.437], dtype=object), array([82.263], dtype=object), array([86.705], dtype=object)]
Predicted Values: [89.50871453 93.81357914 86.34848396]
Explained Variance Score: -2.479805943230146
Maximam Error : 11.550579142109328
Mean Absolute Error : 3.9929365685049967
Mean Squared Error : 44.51604172553798
r2 score : -4.093273823760041
Origional Values: [array([71.888], dtype=object), array([72.416], dtype=object), array([70.399], dtype=object)]
Predicted Values: [71.35736145 73.51398892 74.04909198]
Explained Variance Score: -3.0590155169902946
Maximam Error : 3.650091983068137
Mean Absolute Error : 1.7595731537131674
Mean Squared Error : 4.936776145943987
r2 score : -5.768688633135457
Origional Values: [array([9319.037], dtype=object), array([9264.018], dtype=object), array([9280.847], dtype=object)]
Predicted Values: [ 9616.90158233 10029.75851695 9970.5461847 ]
Explained Variance Score: -78.31261140679975
Maximam Error : 765.74051694851
Mean Absolute Error : 584.4347613262004
Mean Squared Error : 383588.93802581145
r2 score : -722.9376129933538
Origional Values: [array([79.214], dtype=object), array([82.854], dtype=object), array([82.755], dtype=object)]
Predicted Values: [72.22158482 82.08688606 85.82786913]
Explained Variance Score: -5.00082570374328
Maximam Error : 6.992415175666423
Mean Absolute Error : 3.610799415356771
Mean Squared Error : 19.64161949068755
r2 score : -5.852237132054547
Origional Values: [array([8.296], dtype=object), array([8.43], dtype=object), array([8.003], dtype=object)]
Predicted Values: [7.92628808 8.44364348 8.56485715]
Explained Variance Score: -3.5968690875008162
Maximam Error : 0.5618571520669207
Mean Absolute Error : 0.31507085251313277
Mean Squared Error : 0.15085217025854533
r2 score : -3.7448731444949264
Origional Values: [array([15.792], dtype=object), array([15.052], dtype=object), array([15.269], dtype=object)]
Predicted Values: [16.16170382 15.70957374 15.0281557 ]
Explained Variance Score: -0.4544654413364235
Maximam Error : 0.6575737377826911
Mean Absolute Error : 0.4227072861312333
Mean Squared Error : 0.2090300371485552
r2 score : -1.1668179355150392
Origional Values: [array([7.534], dtype=object), array([7.324], dtype=object), array([7.325], dtype=object)]
Predicted Values: [8.67210903 7.58290104 7.39893767]
Explained Variance Score: -21.096543927748286
Maximam Error : 1.1381090313987245
Mean Absolute Error : 0.4903159152973764
Mean Squared Error : 0.4559295655789703
r2 score : -45.74496013090087
Origional Values: [array([101.372], dtype=object), array([98.856], dtype=object), array([99.275], dtype=object)]
Predicted Values: [105.1840778 100.60716091 98.12590193]
Explained Variance Score: -2.4184512451475406
Maximam Error : 3.812077797538066
Mean Absolute Error : 2.2374455903626633
Mean Squared Error : 6.306309341134455
r2 score : -4.205503772022154
Origional Values: [array([74.767], dtype=object), array([66.902], dtype=object), array([64.912], dtype=object)]
Predicted Values: [76.69928348 73.80320221 66.11063917]
Explained Variance Score: 0.6455882969275224
Maximam Error : 6.9012022103180755
Mean Absolute Error : 3.344041620076775
Mean Squared Error : 17.599015749141525
r2 score : 0.027913504366661512
Origional Values: [array([19.019], dtype=object), array([17.642], dtype=object), array([15.602], dtype=object)]
Predicted Values: [16.70346607 18.94453851 17.62236338]
Explained Variance Score: -0.8273612473009049
Maximam Error : 2.315533933960573
Mean Absolute Error : 1.87947860983925
Mean Squared Error : 3.71339072418767
r2 score : -0.8845853405486159
Origional Values: [array([52.903], dtype=object), array([48.379], dtype=object), array([45.243], dtype=object)]
Predicted Values: [50.86533259 52.04383983 47.68442304]
Explained Variance Score: 0.3922273397704775
Maximam Error : 3.664839827564407
Mean Absolute Error : 2.7146434254746175
Mean Squared Error : 7.847895297545446
r2 score : 0.2061845415177017
Origional Values: [array([339.101], dtype=object), array([306.48], dtype=object), array([311.928], dtype=object)]
Predicted Values: [334.38732364 335.21515138 303.06685538]
Explained Variance Score: -0.3915138678987824
Maximam Error : 28.735151380901414
Mean Absolute Error : 14.103324118381371
Mean Squared Error : 308.81585120315395
r2 score : -0.5169575103248638
Origional Values: [array([27063.36], dtype=object), array([27048.59], dtype=object), array([26955.193], dtype=object)]
Predicted Values: [27449.94796047 27954.53594723 27939.27989505]
Explained Variance Score: -29.659171777547503
Maximam Error : 984.0868950489312
Mean Absolute Error : 758.8736009156904
Mean Squared Error : 646205.1091638588
r2 score : -280.7583870559191
Origional Values: [array([799.195], dtype=object), array([756.875], dtype=object), array([765.729], dtype=object)]
Predicted Values: [790.64797662 808.29823325 765.53667724]
Explained Variance Score: -1.117651232611101
Maximam Error : 51.4232332457168
Mean Absolute Error : 20.05419312721972
Mean Squared Error : 905.8125046994875
r2 score : -1.7271198148381832
Origional Values: [array([78.992], dtype=object), array([79.685], dtype=object), array([76.874], dtype=object)]
Predicted Values: [85.34510979 76.93674279 77.60520652]
Explained Variance Score: -8.834347842575763
Maximam Error : 6.353109790527014
Mean Absolute Error : 3.277524506804274
Mean Squared Error : 16.149861563153255
r2 score : -10.295457832367786
Origional Values: [array([47.789], dtype=object), array([47.693], dtype=object), array([50.899], dtype=object)]
Predicted Values: [49.44470034 47.13647741 47.04157831]
Explained Variance Score: -1.3138754245579745
Maximam Error : 3.85742168817454
Mean Absolute Error : 2.0232148713650537
Mean Squared Error : 5.976921027402976
r2 score : -1.695037956715431
Origional Values: [array([3.751], dtype=object), array([3.855], dtype=object), array([4.131], dtype=object)]
Predicted Values: [3.60306998 3.72963552 3.81401254]
Explained Variance Score: 0.7155952448490572
Maximam Error : 0.31698745729922173
Mean Absolute Error : 0.1967606532337546
Mean Squared Error : 0.04602686411696128
r2 score : -0.7902165029588353
Origional Values: [array([1866.712], dtype=object), array([2030.043], dtype=object), array([2043.407], dtype=object)]
Predicted Values: [1958.69511344 2011.87257783 2187.86042936]
Explained Variance Score: 0.2882958643212693
Maximam Error : 144.45342935683107
Mean Absolute Error : 84.868988322172
Mean Squared Error : 9885.950217683256
r2 score : -0.531999893477576
Origional Values: [array([440.104], dtype=object), array([477.653], dtype=object), array([479.376], dtype=object)]
Predicted Values: [454.93769461 458.12873928 497.18367279]
Explained Variance Score: 0.12594780787751292
Maximam Error : 19.52426072382724
Mean Absolute Error : 17.388542709288345
Mean Squared Error : 306.11615430351776
r2 score : 0.06772525233527205
Origional Values: [array([51.398], dtype=object), array([51.303], dtype=object), array([52.75], dtype=object)]
Predicted Values: [51.67315943 50.68899117 50.59660384]
Explained Variance Score: -1.304456783399393
Maximam Error : 2.1533961628928324
Mean Absolute Error : 1.014188140735553
Mean Squared Error : 1.6966115289034776
r2 score : -2.884640431423131
Origional Values: [array([71.43], dtype=object), array([69.624], dtype=object), array([70.97], dtype=object)]
Predicted Values: [82.007132 73.66918718 71.82493386]
Explained Variance Score: -26.88391570761987
Maximam Error : 10.577131998748797
Mean Absolute Error : 5.159084344452741
Mean Squared Error : 42.99005750504612
r2 score : -72.20984515004314
Origional Values: [array([360.773], dtype=object), array([340.683], dtype=object), array([346.837], dtype=object)]
Predicted Values: [383.54235639 354.89292626 335.15874773]
Explained Variance Score: -2.0362182343340596
Maximam Error : 22.769356388388474
Mean Absolute Error : 16.21917830857035
Mean Squared Error : 285.5823903079802
r2 score : -3.0432188364413433
Origional Values: [array([1270.62], dtype=object), array([1235.109], dtype=object), array([1202.308], dtype=object)]
Predicted Values: [1262.44118542 1284.17881968 1248.31535888]
Explained Variance Score: 0.1114505122493693
Maximam Error : 49.06981967967249
Mean Absolute Error : 34.41866438009932
Mean Squared Error : 1530.4724274217333
r2 score : -0.9667764099930518
Origional Values: [array([250.07], dtype=object), array([230.596], dtype=object), array([226.456], dtype=object)]
Predicted Values: [247.15149121 263.00168042 242.54257094]
Explained Variance Score: -0.9657338524744523
Maximam Error : 32.405680415537404
Mean Absolute Error : 17.136920049968314
Mean Squared Error : 439.14119382732883
r2 score : -3.1428532978508974
Origional Values: [array([624.611], dtype=object), array([616.627], dtype=object), array([632.478], dtype=object)]
Predicted Values: [649.08504474 647.42192308 639.15395088]
Explained Variance Score: -1.4899973052461997
Maximam Error : 30.794923076238206
Mean Absolute Error : 20.64830623087937
Mean Squared Error : 530.6248244174203
r2 score : -11.671195606552144
Origional Values: [array([7.383], dtype=object), array([7.122], dtype=object), array([7.131], dtype=object)]
Predicted Values: [7.81521983 7.5590592 7.3316513 ]
Explained Variance Score: 0.16832870468837546
Maximam Error : 0.43705920065160964
Mean Absolute Error : 0.3566434417651472
Mean Squared Error : 0.1393652222896802
r2 score : -8.523385423649053
Origional Values: [array([10.531], dtype=object), array([9.838], dtype=object), array([10.284], dtype=object)]
Predicted Values: [11.06205879 10.54547569 9.89999841]
Explained Variance Score: -1.7828335921842453
Maximam Error : 0.7074756896407077
Mean Absolute Error : 0.5408453554888103
Mean Squared Error : 0.31000083577842513
r2 score : -2.7693941181476434
Origional Values: [array([10.587], dtype=object), array([10.017], dtype=object), array([9.593], dtype=object)]
Predicted Values: [11.14700019 10.56328896 10.02752719]
Explained Variance Score: 0.9809593781679855
Maximam Error : 0.5600001863363708
Mean Absolute Error : 0.5136054426212583
Mean Squared Error : 0.2669485689897464
r2 score : -0.6095114937829391
Origional Values: [array([224.23], dtype=object), array([236.862], dtype=object), array([238.435], dtype=object)]
Predicted Values: [212.91266679 231.3395463 244.33973295]
Explained Variance Score: -0.2664394532106906
Maximam Error : 11.317333213144195
Mean Absolute Error : 7.581506622120126
Mean Squared Error : 64.4817990585779
r2 score : -0.5951024276571617
Origional Values: [array([3.67], dtype=object), array([3.61], dtype=object), array([3.121], dtype=object)]
Predicted Values: [3.96325708 3.78586884 3.73790874]
Explained Variance Score: 0.42467574301144284
Maximam Error : 0.616908741293626
Mean Absolute Error : 0.3620115562427258
Mean Squared Error : 0.1658353207822944
r2 score : -1.7429839025818663
Origional Values: [array([459.256], dtype=object), array([444.256], dtype=object), array([453.009], dtype=object)]
Predicted Values: [478.65391843 469.19575539 453.90283251]
Explained Variance Score: -1.7927416316185885
Maximam Error : 24.93975539399929
Mean Absolute Error : 15.077168779777537
Mean Squared Error : 333.02319170988795
r2 score : -7.798756948718191
Origional Values: [array([55.286], dtype=object), array([57.194], dtype=object), array([59.222], dtype=object)]
Predicted Values: [58.34323884 57.7061659 59.66987377]
Explained Variance Score: 0.4282590170336281
Maximam Error : 3.057238840262542
Mean Absolute Error : 1.3390928361886125
Mean Squared Error : 3.269871382592935
r2 score : -0.26601019298042705
Origional Values: [array([183.301], dtype=object), array([175.71], dtype=object), array([181.652], dtype=object)]
Predicted Values: [182.24943599 182.56523639 175.02674311]
Explained Variance Score: -1.8782821453804157
Maximam Error : 6.8552363916759305
Mean Absolute Error : 4.844019097057327
Mean Squared Error : 30.664693894852757
r2 score : -1.8853391468468899
Origional Values: [array([33.744], dtype=object), array([34.018], dtype=object), array([33.943], dtype=object)]
Predicted Values: [33.65450559 33.8276075 34.09557316]
Explained Variance Score: -0.5494553125308614
Maximam Error : 0.19039250275898212
Mean Absolute Error : 0.14415335477318555
Mean Squared Error : 0.02251237386672569
r2 score : -0.6841894964383881
Origional Values: [array([51.011], dtype=object), array([52.21], dtype=object), array([52.02], dtype=object)]
Predicted Values: [50.94378268 51.46476354 52.6637195 ]
Explained Variance Score: -0.16155416493510777
Maximam Error : 0.745236459882193
Mean Absolute Error : 0.48539109270767256
Mean Squared Error : 0.3247567799055184
r2 score : -0.17298022826622517
Origional Values: [array([44.85], dtype=object), array([48.151], dtype=object), array([49.194], dtype=object)]
Predicted Values: [46.73989804 47.73251155 51.19983134]
Explained Variance Score: 0.6363800690789846
Maximam Error : 2.0058313386496494
Mean Absolute Error : 1.4380726090621916
Mean Squared Error : 2.590068844879793
r2 score : 0.24450554919038758
Origional Values: [array([96.2], dtype=object), array([102.035], dtype=object), array([110.825], dtype=object)]
Predicted Values: [ 88.48496921 98.46562912 104.4009278 ]
Explained Variance Score: 0.9169673187592176
Maximam Error : 7.715030788661892
Mean Absolute Error : 5.902824621321244
Mean Squared Error : 37.84360404932416
r2 score : -0.04732593529625939
Origional Values: [array([293.7], dtype=object), array([280.762], dtype=object), array([283.693], dtype=object)]
Predicted Values: [298.86890044 294.40385428 281.45702166]
Explained Variance Score: -0.3715960642522713
Maximam Error : 13.64185428455039
Mean Absolute Error : 7.01557768875053
Mean Squared Error : 72.60577307684775
r2 score : -1.3665278752957026
Origional Values: [array([46.816], dtype=object), array([46.064], dtype=object), array([50.077], dtype=object)]
Predicted Values: [47.20667728 45.33214862 44.61440649]
Explained Variance Score: -1.1206126421258595
Maximam Error : 5.462593511278875
Mean Absolute Error : 2.1950407238087735
Mean Squared Error : 10.176054348934878
r2 score : -2.3542770334560115
Origional Values: [array([70.074], dtype=object), array([69.792], dtype=object), array([70.978], dtype=object)]
Predicted Values: [78.75779547 69.09779138 68.8211185 ]
Explained Variance Score: -89.13302875913998
Maximam Error : 8.683795470890033
Mean Absolute Error : 3.8449618644596533
Mean Squared Error : 26.847455734580777
r2 score : -103.90310645569281
Origional Values: [array([1531.207], dtype=object), array([1511.148], dtype=object), array([1487.625], dtype=object)]
Predicted Values: [1589.37850092 1553.62500348 1533.27891124]
Explained Variance Score: 0.8553108091314257
Maximam Error : 58.171500924319844
Mean Absolute Error : 48.76747187844186
Mean Squared Error : 2424.166318382715
r2 score : -6.641625820769253
Origional Values: [array([478.436], dtype=object), array([515.782], dtype=object), array([541.376], dtype=object)]
Predicted Values: [505.50773637 514.27615634 554.39197307]
Explained Variance Score: 0.7961935026604271
Maximam Error : 27.07173637463262
Mean Absolute Error : 13.864517702887781
Mean Squared Error : 301.52067683779586
r2 score : 0.5485631830274442
Origional Values: [array([122.854], dtype=object), array([124.33], dtype=object), array([122.394], dtype=object)]
Predicted Values: [127.66251437 131.59390867 133.16731919]
Explained Variance Score: -7.784853428096245
Maximam Error : 10.773319187526596
Mean Absolute Error : 7.615247410219321
Mean Squared Error : 63.98352865460617
r2 score : -92.81333344163616
Origional Values: [array([31.057], dtype=object), array([28.94], dtype=object), array([29.152], dtype=object)]
Predicted Values: [30.23140936 30.50972802 28.49191774]
Explained Variance Score: -0.31650915192046525
Maximam Error : 1.5697280170695933
Mean Absolute Error : 1.0184669747766861
Mean Squared Error : 1.193784850935373
r2 score : -0.31737545375472864
Origional Values: [array([14.389], dtype=object), array([12.786], dtype=object), array([12.892], dtype=object)]
Predicted Values: [14.9365645 14.35833494 12.82118403]
Explained Variance Score: 0.14296580842358542
Maximam Error : 1.5723349378500302
Mean Absolute Error : 0.7302384698668375
Mean Squared Error : 0.9256929815927123
r2 score : -0.7278077756677024
Origional Values: [array([425.375], dtype=object), array([439.015], dtype=object), array([414.513], dtype=object)]
Predicted Values: [423.51344293 433.77315114 447.65908286]
Explained Variance Score: -1.9971832483639038
Maximam Error : 33.146082860498495
Mean Absolute Error : 13.416496265416393
Mean Squared Error : 376.53506108187617
r2 score : -2.747111967987271
Origional Values: [array([251.47], dtype=object), array([248.941], dtype=object), array([263.328], dtype=object)]
Predicted Values: [270.57843753 244.46688619 242.01892825]
Explained Variance Score: -5.98635901159501
Maximam Error : 21.309071746319887
Mean Absolute Error : 14.96387436283687
Mean Squared Error : 279.74220601837305
r2 score : -6.112215070102808
Origional Values: [array([45.003], dtype=object), array([45.367], dtype=object), array([43.803], dtype=object)]
Predicted Values: [44.45425229 43.76613809 44.11209606]
Explained Variance Score: -0.36634414782943026
Maximam Error : 1.6008619129699042
Mean Absolute Error : 0.8195685601886803
Mean Squared Error : 0.9864744291753921
r2 score : -1.2092986455401586
Origional Values: [array([50.448], dtype=object), array([46.045], dtype=object), array([44.867], dtype=object)]
Predicted Values: [49.84537896 50.41237631 46.10808439]
Explained Variance Score: 0.270558205292989
Maximam Error : 4.36737631015923
Mean Absolute Error : 2.0703605790317234
Mean Squared Error : 6.992472804553052
r2 score : -0.2120618353370911
Origional Values: [array([264.26], dtype=object), array([266.809], dtype=object), array([268.224], dtype=object)]
Predicted Values: [263.35332303 264.24750827 266.78567996]
Explained Variance Score: 0.8231287042277498
Maximam Error : 2.5614917302979165
Mean Absolute Error : 1.6354962477032966
Mean Squared Error : 3.150689186092945
r2 score : -0.17111857357969562
Origional Values: [array([262.577], dtype=object), array([259.148], dtype=object), array([267.676], dtype=object)]
Predicted Values: [259.66573746 270.11685787 266.59549677]
Explained Variance Score: -2.0881810215622565
Maximam Error : 10.968857867779548
Mean Absolute Error : 4.986874547393995
Mean Squared Error : 43.31959325189627
r2 score : -2.5287836270276163
Origional Values: [array([25.545], dtype=object), array([26.987], dtype=object), array([27.462], dtype=object)]
Predicted Values: [26.65142063 26.54366459 28.0095533 ]
Explained Variance Score: 0.38193774368568645
Maximam Error : 1.1064206251921753
Mean Absolute Error : 0.6991031106736662
Mean Squared Error : 0.5735091661510267
r2 score : 0.13684150490039193
Origional Values: [array([297.813], dtype=object), array([321.668], dtype=object), array([336.883], dtype=object)]
Predicted Values: [323.81804935 311.4869143 336.35034313]
Explained Variance Score: 0.09464617710542522
Maximam Error : 26.00504934810158
Mean Absolute Error : 12.239597303667475
Mean Squared Error : 260.06694029577534
r2 score : -0.005835918949881336
Origional Values: [array([482.462], dtype=object), array([444.989], dtype=object), array([430.787], dtype=object)]
Predicted Values: [490.21160275 475.89337991 438.96147157]
Explained Variance Score: 0.7537612790532463
Maximam Error : 30.904379909806778
Mean Absolute Error : 15.609484742358367
Mean Squared Error : 360.65300859996455
r2 score : 0.24094857489601718
Origional Values: [array([5146.637], dtype=object), array([5155.535], dtype=object), array([5020.004], dtype=object)]
Predicted Values: [4944.30270442 5076.36681507 5085.14222566]
Explained Variance Score: -2.118448609285483
Maximam Error : 202.3342955784983
Mean Absolute Error : 115.5469020558897
Mean Squared Error : 17149.919038090982
r2 score : -3.4760010855293473
Origional Values: [array([133.605], dtype=object), array([147.779], dtype=object), array([173.294], dtype=object)]
Predicted Values: [136.74778032 141.90433883 156.86627542]
Explained Variance Score: 0.7628124332772477
Maximam Error : 16.427724576112126
Mean Absolute Error : 8.481722023176795
Mean Squared Error : 104.75294892821252
r2 score : 0.6115680246240985
Origional Values: [array([32287.8], dtype=object), array([32327.8], dtype=object), array([32276.0], dtype=object)]
Predicted Values: [32516.17664955 33151.3054829 33192.37427264]
Explained Variance Score: -188.0666932979837
Maximam Error : 916.3742726448181
Mean Absolute Error : 656.0854683666063
Mean Squared Error : 523352.9939992125
r2 score : -1064.0533062881032
seeno=Y.index[-3:]
i=0
while i<len(VVIP1):
plt.plot(seeno,VVIP1[i])
plt.plot(seeno,VVIP2[i])
plt.plot(seeno,VVIP3[i])
plt.plot(seeno,VVIP4[i])
plt.legend(['Predicted LR','Predicted RF','Predicted MLP'
,'Actual CO2'], title='Presentation',
bbox_to_anchor=(1.05, 1), loc='upper left')
plt.title(str(CO2_PROD_FINAL.columns[i]))
i=i+1
plt.show()
i=0
list_2016=list()
list_2017=list()
list_2018=list()
list_2019=list()
while i<len(CO2_PROD_FINAL.T):
series=CO2_PROD_FINAL[CO2_PROD_FINAL.columns[i]]
X = pd.DataFrame(series[:10])
Y = pd.DataFrame(series[-10:])
Model = MLPRegressor()
Model = Model.fit(X, Y)
train_fit = Model.predict(Y[-1:])
list_2016.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2017.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2018.append(train_fit[0])
train_fit=Model.predict(pd.DataFrame(train_fit))
list_2019.append(train_fit[0])
i=i+1
CO2_PROD['F2016']=list_2016
CO2_PROD['F2017']=list_2017
CO2_PROD['F2018']=list_2018
CO2_PROD['F2019']=list_2019
CO2_PROD.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 6 | Argentina | AR | ARG | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 152.27 | 161.49 | 168.48 | ... | 177.26 | 185.18 | 189.64 | 184.82 | 188.74 | 195.03 | 201.95 | 209.09 | 216.48 | 224.11 |
| 11 | 12 | Australia | AU | AUS | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 382.10 | 387.60 | 400.35 | ... | 400.08 | 398.54 | 397.91 | 395.40 | 385.99 | 392.49 | 394.15 | 395.81 | 397.48 | 399.16 |
| 17 | 18 | Austria | AT | AUT | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 75.27 | 73.21 | 71.77 | ... | 69.74 | 68.31 | 65.61 | 66.58 | 62.77 | 64.26 | 63.97 | 63.69 | 63.41 | 63.13 |
| 23 | 24 | Belgium | BE | BEL | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 118.87 | 118.00 | 114.79 | ... | 114.63 | 103.33 | 102.29 | 101.64 | 95.11 | 101.30 | 99.92 | 98.56 | 97.25 | 95.96 |
| 29 | 30 | Brazil | BR | BRA | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 321.14 | 324.48 | 340.54 | ... | 381.49 | 400.36 | 431.96 | 460.64 | 484.94 | 461.15 | 478.78 | 497.04 | 515.98 | 535.61 |
5 rows × 22 columns
CO2_Grass_IMP.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 5 | Argentina | AR | ARG | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 28.14 | 32.24 | 37.97 | ... | 40.14 | 47.78 | 44.06 | 47.05 | 41.76 | 42.24 | 43.46 | 44.70 | 45.97 | 47.26 |
| 22 | 23 | Belgium | BE | BEL | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 87.83 | 92.67 | 99.73 | ... | 89.81 | 99.17 | 90.75 | 88.39 | 87.94 | 86.55 | 86.68 | 86.82 | 86.96 | 87.09 |
| 76 | 77 | Colombia | CO | COL | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 21.10 | 23.54 | 27.97 | ... | 29.22 | 36.18 | 39.56 | 39.71 | 42.57 | 36.09 | 37.32 | 38.55 | 39.80 | 41.05 |
| 130 | 131 | G20 | None | G20 | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 1484.69 | 1600.61 | 1629.40 | ... | 1558.79 | 1708.00 | 1667.58 | 1643.66 | 1605.44 | 1469.53 | 1469.83 | 1470.12 | 1470.41 | 1470.70 |
| 172 | 173 | Ireland | IE | IRL | C02 Emissions Embodied in Gross Imports | ECBPDTCETM | Millions of metric tons | 41.17 | 45.02 | 48.96 | ... | 38.46 | 34.98 | 34.71 | 36.59 | 39.88 | 40.46 | 40.54 | 40.62 | 40.69 | 40.77 |
5 rows × 22 columns
CO2_Grass_Exp.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 3 | Argentina | AR | ARG | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 39.18 | 40.23 | 39.51 | ... | 34.34 | 34.41 | 30.52 | 26.57 | 27.17 | 21.29 | 20.84 | 20.42 | 20.02 | 19.65 |
| 20 | 21 | Belgium | BE | BEL | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 75.41 | 81.13 | 85.17 | ... | 77.07 | 80.30 | 75.38 | 72.75 | 71.16 | 70.60 | 70.37 | 70.14 | 69.91 | 69.69 |
| 74 | 75 | Colombia | CO | COL | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 13.85 | 13.68 | 13.77 | ... | 14.60 | 16.40 | 16.99 | 19.19 | 21.54 | 21.50 | 22.42 | 23.34 | 24.25 | 25.15 |
| 128 | 129 | G20 | None | G20 | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 1256.47 | 1426.53 | 1582.22 | ... | 1690.87 | 1808.02 | 1871.12 | 1947.10 | 1882.40 | 1737.10 | 1791.39 | 1847.37 | 1905.08 | 1964.59 |
| 170 | 171 | Ireland | IE | IRL | C02 Emissions Embodied in Gross Exports | ECBPDTCETX | Millions of metric tons | 39.15 | 41.20 | 42.44 | ... | 38.52 | 38.60 | 38.68 | 40.62 | 42.75 | 46.68 | 47.75 | 48.83 | 49.92 | 51.01 |
5 rows × 22 columns
CO2_FINAL_DF.head()
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Argentina | AR | ARG | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 141.18 | 153.44 | 166.89 | ... | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 | 225.07 | 234.45 | 244.17 | 254.25 |
| 7 | 8 | Australia | AU | AUS | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 418.42 | 420.98 | 444.59 | ... | 446.21 | 450.35 | 469.37 | 448.02 | 423.10 | 426.40 | 432.60 | 438.87 | 445.21 | 451.64 |
| 13 | 14 | Austria | AT | AUT | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 95.26 | 95.39 | 94.53 | ... | 89.74 | 90.34 | 86.19 | 86.53 | 82.95 | 83.44 | 82.90 | 82.36 | 81.84 | 81.31 |
| 19 | 20 | Belgium | BE | BEL | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 131.71 | 129.89 | 129.69 | ... | 127.70 | 122.48 | 117.93 | 117.66 | 112.33 | 117.83 | 116.80 | 115.79 | 114.79 | 113.81 |
| 25 | 26 | Brazil | BR | BRA | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 312.82 | 330.87 | 363.68 | ... | 458.99 | 486.29 | 510.59 | 542.57 | 555.96 | 475.38 | 492.11 | 509.40 | 527.29 | 545.78 |
5 rows × 22 columns
Combined4Cases=pd.concat([CO2_FINAL_DF,CO2_Grass_Exp,CO2_Grass_IMP,CO2_PROD])
Combined4Cases.to_csv("Fully_Populated_Dataset_CO2.csv")
Combined4Cases
| ObjectId | Country | ISO2 | ISO3 | Indicator | Code | Unit | F2005 | F2006 | F2007 | ... | F2010 | F2011 | F2012 | F2013 | F2014 | F2015 | F2016 | F2017 | F2018 | F2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Argentina | AR | ARG | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 141.18 | 153.44 | 166.89 | ... | 183.06 | 198.55 | 203.19 | 205.33 | 203.33 | 216.03 | 225.07 | 234.45 | 244.17 | 254.25 |
| 7 | 8 | Australia | AU | AUS | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 418.42 | 420.98 | 444.59 | ... | 446.21 | 450.35 | 469.37 | 448.02 | 423.10 | 426.40 | 432.60 | 438.87 | 445.21 | 451.64 |
| 13 | 14 | Austria | AT | AUT | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 95.26 | 95.39 | 94.53 | ... | 89.74 | 90.34 | 86.19 | 86.53 | 82.95 | 83.44 | 82.90 | 82.36 | 81.84 | 81.31 |
| 19 | 20 | Belgium | BE | BEL | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 131.71 | 129.89 | 129.69 | ... | 127.70 | 122.48 | 117.93 | 117.66 | 112.33 | 117.83 | 116.80 | 115.79 | 114.79 | 113.81 |
| 25 | 26 | Brazil | BR | BRA | C02 Emissions Embodied in Final Domestic Demand | ECBPDTCEFDD | Millions of metric tons | 312.82 | 330.87 | 363.68 | ... | 458.99 | 486.29 | 510.59 | 542.57 | 555.96 | 475.38 | 492.11 | 509.40 | 527.29 | 545.78 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 371 | 372 | Turkey | TR | TUR | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 227.13 | 252.59 | 278.48 | ... | 279.04 | 298.52 | 309.64 | 297.81 | 321.67 | 336.88 | 351.84 | 367.41 | 383.63 | 400.53 |
| 377 | 378 | United Kingdom | GB | GBR | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 569.09 | 571.22 | 561.28 | ... | 512.23 | 476.07 | 496.99 | 482.46 | 444.99 | 430.79 | 419.84 | 409.20 | 398.84 | 388.77 |
| 383 | 384 | United States | US | USA | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 5833.58 | 5723.61 | 5822.52 | ... | 5463.63 | 5248.63 | 5012.73 | 5146.64 | 5155.53 | 5020.00 | 4989.98 | 4960.14 | 4930.48 | 4901.01 |
| 389 | 390 | Vietnam | VN | VNM | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 82.13 | 84.46 | 93.13 | ... | 129.01 | 128.95 | 128.72 | 133.60 | 147.78 | 173.29 | 190.72 | 209.84 | 230.82 | 253.85 |
| 395 | 396 | World | None | WLD | C02 Emissions Embodied in Production | ECBPDTCEVA | Millions of metric tons | 27069.60 | 27924.30 | 28979.70 | ... | 30489.90 | 31338.30 | 31669.20 | 32287.80 | 32327.80 | 32276.00 | 33143.59 | 34034.50 | 34949.33 | 35888.74 |
264 rows × 22 columns
Major_Exp=CO2_Grass_Exp.sort_values("F2019",ascending =False)
Major_IMP=CO2_Grass_IMP.sort_values("F2019",ascending =False)
See=Major_IMP[['Country','F2019']]
See.columns=['Country','IMPORTS']
See.index=See['Country']
See1=Major_Exp[['Country','F2019']]
See1.columns=['Country','Exports']
See1.index=See1['Country']
See['Exports']=See1['Exports']
See
| Country | IMPORTS | Exports | |
|---|---|---|---|
| Country | |||
| United States | United States | 1973.55 | 585.82 |
| G20 | G20 | 1470.70 | 1964.59 |
| China, P.R.: Mainland | China, P.R.: Mainland | 676.24 | 2212.07 |
| Germany | Germany | 490.40 | 324.40 |
| France | France | 411.94 | 155.99 |
| ... | ... | ... | ... |
| Brunei Darussalam | Brunei Darussalam | 3.51 | 3.07 |
| Malta | Malta | 3.16 | 2.54 |
| Iceland | Iceland | 2.52 | 3.91 |
| World | World | 0.00 | 0.03 |
| India | India | -447.79 | 455.37 |
66 rows × 3 columns
Case10=See.head(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="Imports"
))
fig.update_layout(
title="Top 10 Importers",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="Imports"
))
fig.update_layout(
title="Least 10 Importers",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See=See.sort_values("Exports")
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="Imports"
))
fig.update_layout(
title="Top 10 Exporters",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See=See.sort_values("Exports")
Case10=See.head(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="Imports"
))
fig.update_layout(
title="Least 10 Exporters",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See1=CO2_FINAL_DF[['Country','F2019']]
See1.columns=['Country','C02 Emissions Embodied in Final Domestic Demand']
See1.index=See1['Country']
See['C02 Emissions Embodied in Final Domestic Demand']=See1['C02 Emissions Embodied in Final Domestic Demand']
See1=CO2_PROD[['Country','F2019']]
See1.columns=['Country','C02 Emissions Embodied in Production']
See1.index=See1['Country']
See['C02 Emissions Embodied in Production']=See1['C02 Emissions Embodied in Production']
See
| Country | IMPORTS | Exports | C02 Emissions Embodied in Final Domestic Demand | C02 Emissions Embodied in Production | |
|---|---|---|---|---|---|
| Country | |||||
| World | World | 0.00 | 0.03 | 35604.86 | 35888.74 |
| Malta | Malta | 3.16 | 2.54 | 2.90 | 3.30 |
| Brunei Darussalam | Brunei Darussalam | 3.51 | 3.07 | 7.21 | 7.41 |
| Costa Rica | Costa Rica | 8.89 | 3.70 | 15.64 | 8.72 |
| Iceland | Iceland | 2.52 | 3.91 | 2.69 | 4.10 |
| ... | ... | ... | ... | ... | ... |
| Russian Federation | Russian Federation | 174.05 | 395.08 | 1240.29 | 1556.81 |
| India | India | -447.79 | 455.37 | 2494.62 | 2662.76 |
| United States | United States | 1973.55 | 585.82 | 5537.58 | 4901.01 |
| G20 | G20 | 1470.70 | 1964.59 | 29266.84 | 29949.05 |
| China, P.R.: Mainland | China, P.R.: Mainland | 676.24 | 2212.07 | 10159.84 | 11527.51 |
66 rows × 5 columns
See['C02 Emissions Embodied in Final Domestic Demand']
Country
World 35604.86
Malta 2.90
Brunei Darussalam 7.21
Costa Rica 15.64
Iceland 2.69
...
Russian Federation 1240.29
India 2494.62
United States 5537.58
G20 29266.84
China, P.R.: Mainland 10159.84
Name: C02 Emissions Embodied in Final Domestic Demand, Length: 66, dtype: float64
See=See.sort_values("Exports")
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10['C02 Emissions Embodied in Production'],
name="C02 Emissions Embodied in Production"
))
fig.update_layout(
title="Top 10 Exporters and C02 Emissions Embodied in Production",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See=See.sort_values("Exports")
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.Exports,
name="Exports" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10['C02 Emissions Embodied in Final Domestic Demand'],
name="C02 Emissions Embodied in Final Domestic Demand"
))
fig.update_layout(
title="Top 10 Exporters and C02 Emissions Embodied in Final Domestic Demand",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See=See.sort_values("C02 Emissions Embodied in Final Domestic Demand")
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="IMPORTS" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10['C02 Emissions Embodied in Final Domestic Demand'],
name="C02 Emissions Embodied in Final Domestic Demand"
))
fig.update_layout(
title="Top Countires with High C02 Emissions Embodied in Final Domestic Demand",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)
See=See.sort_values("C02 Emissions Embodied in Production")
Case10=See.tail(10)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10.IMPORTS,
name="IMPORTS" # this sets its legend entry
))
fig.add_trace(go.Scatter(
x=Case10.index,
y=Case10['C02 Emissions Embodied in Production'],
name="C02 Emissions Embodied in Production"
))
fig.update_layout(
title="Top Countires with High C02 Emissions Embodied in Production",
xaxis_title="Countries",
yaxis_title="CO2(Millions of metric tons)",
legend_title="Type",
font=dict(
family="Courier New, monospace",
size=12,
color="RebeccaPurple"
)
)